CN116735507A - Cracking saline-alkali soil conductivity measurement method based on unmanned aerial vehicle low-altitude remote sensing image - Google Patents
Cracking saline-alkali soil conductivity measurement method based on unmanned aerial vehicle low-altitude remote sensing image Download PDFInfo
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Abstract
The invention discloses a cracking saline-alkali soil conductivity measurement method based on a low-altitude remote sensing image of an unmanned aerial vehicle, and belongs to the field of remote sensing measurement. According to the invention, the unmanned aerial vehicle is utilized to obtain remote sensing images of the surface of the salinized soil at different heights, the phenomenon of water loss shrinkage cracking of the surface of the viscous salinized soil is utilized, the characteristic parameters of the surface cracks of the soil are extracted by combining with the optical image to serve as soil indexes, the diagnostic spectral characteristic reflectivity of main salinity minerals of the salinized soil and the mathematical transformation result of the reflectivity are used as soil salinity response factors, a quantitative relation between the response factors and the soil conductivity is established according to an artificial neural network model, and the accurate measurement of the salinized soil conductivity by utilizing the low-altitude remote sensing image data of the unmanned aerial vehicle is realized. The method has the advantages of low cost, small loss, capability of realizing real-time and synchronous observation of the conductivity of the soil with a large area and strong maneuverability.
Description
Technical Field
The invention belongs to the field of remote sensing measurement, and particularly relates to a method for measuring the conductivity of cracked saline-alkali soil based on a low-altitude remote sensing image of an unmanned aerial vehicle.
Background
The method for rapidly and accurately measuring the salt content of the soil and the distribution thereof have very important practical significance in the aspects of optimizing soil management measures, promoting reasonable utilization, improving soil conditions, ensuring grain safety, improving ecological environment and the like. The conductivity of soil is a digital representation of the conductivity of a soil solution, i.e., the ability of the soil to conduct electricity. The conductivity of soil is generally regarded as an accurate quantitative index of the salt content of the soil, and is also one of the international evaluation standards of soil salinization. This is because when the soil humidity is kept constant, the salt minerals are considered as strong electrolytes in the soil, various dissolved salts in the soil solution exist in an ionic state, and they all have conductivity, and an increase in the content of the salt minerals increases the osmotic pressure of the soil solution, thereby causing an increase in the conductivity value. Thus, the conductivity can be utilized to directly reflect the salt content level of the soil.
Currently, the measurement of soil conductivity is largely divided into two main types, contact and non-contact. The method is mainly realized by an electrode type sensor, and the method needs to form a loop by a voltmeter, an electrode, a constant current power supply and soil to realize the measurement of the conductivity. However, the electrode method requires digging or perforating holes in the soil to embed the salt sensor or electrode for measurement into soil layers of different depths, and the instrument probe must be ensured to be in good contact with the soil during the measurement. The method is time-consuming and labor-consuming, is greatly influenced by the water content of the soil and the meteorological conditions, has high use difficulty of instruments and is easy to cause operation errors. The non-contact conductivity measurement is realized mainly by a geodetic conductivity meter, which is based on electromagnetic field theory and obtains the soil conductivity value by measuring and determining the relative relation between primary and secondary magnetic fields. However, in practical applications, the apparatus itself is expensive, and the larger volume is not portable and easy to handle. In addition, the accuracy of the conductivity measurement result of the geodetic apparatus is easily affected by factors such as soil physical properties including soil texture, soil moisture, soil temperature, and the like, and is also affected by the measurement environments such as air temperature and pressure. Meanwhile, the conductivity measurement result of the geodetic conductivity meter is often an overall representation of the salt content condition of a soil profile at a certain depth, and has poor sensitivity to the salt content condition of a common soil surface layer.
Disclosure of Invention
Based on the defects, the invention provides a cracking saline-alkali soil conductivity measurement method based on the unmanned aerial vehicle low-altitude remote sensing image, which solves the defects that the traditional laboratory of the existing soil conductivity is in measurement hysteresis and the limitation that the conductivity of the soil is easily interfered by environmental factors and measurement artifacts by a conductivity method is overcome, and realizes the accurate measurement of the conductivity of the saline-alkali soil by utilizing the unmanned aerial vehicle low-altitude remote sensing image data.
The technology adopted by the invention is as follows: a method for measuring the conductivity of cracked saline-alkali soil based on unmanned aerial vehicle low-altitude remote sensing images comprises the following steps:
step 1, unmanned aerial vehicle remote sensing image acquisition
The method comprises the steps that the adopted equipment comprises a multi-rotor unmanned aerial vehicle, a handheld GPS and a rectangular metal calibration frame with the inner diameter of 1m multiplied by 1m, wherein the unmanned aerial vehicle is provided with a high-definition CCD lens and a hyperspectral imaging spectrometer, a rectangular research area is selected to be divided into four sub-areas for shooting of a CCD high-definition remote sensing image of the unmanned aerial vehicle on the soil surface and shooting of the hyperspectral remote sensing image, sampling points are selected for collecting soil samples, the conductivity true value of each soil sample is measured, and all conductivity measurement results are stored as a conductivity data set E to be used as a modeling sample set of a soil conductivity prediction model of the whole research area;
Step 2, preprocessing of unmanned aerial vehicle remote sensing images
Performing image mosaic, geometric correction and cutting on the CCD high-definition remote sensing images shot by the four rectangular subregions to generate a rectangular CCD high-definition remote sensing image map Z1 corresponding to the whole rectangular research region; performing image mosaic, geometric correction and cutting on the hyperspectral remote sensing images shot by the four rectangular subregions to generate a rectangular hyperspectral remote sensing image map Z2 corresponding to the whole rectangular research region;
step 3, extracting spectral characteristic parameters
Searching full-band hyperspectral reflectivity data of a sampling point according to position information of the sampling point, calculating a characteristic spectrum band of the sampling point on the basis, and further calculating first derivative, second derivative, logarithm, reciprocal and square root mathematical transformation parameters of the characteristic band spectrum reflectivity data as spectrum characteristic parameters to form a spectrum reflectivity characteristic parameter data set of the soil sample point; step 4, extracting characteristic parameters of soil surface
Determining the row number of image pixels corresponding to a 1m multiplied by 1m earth surface area according to the position information of sampling points, taking each sample point as a center, determining the image size of the soil sample surface according to the earth surface area size, cutting all sample point data according to the size, carrying out graying treatment on a cutting result image, calculating 256 gray levels according to the treated gray level image, extracting a contrast ratio, an angular second moment, consistency and four statistical texture characteristic parameters according to the gray level symbiotic matrix, wherein the step length of the gray level symbiotic matrix is in the four directions of 0, 45, 90 and 135 degrees, and synthesizing the four statistical texture characteristic parameters into a crack characteristic parameter data set of all soil sample points in a research area;
Step 5, establishing a conductivity prediction model
The method comprises the steps of establishing a salinized soil conductivity neural network prediction model based on unmanned aerial vehicle remote sensing image data by using a neural network toolbox of computer MATLAB software, taking a spectral characteristic parameter data set T of all sample points and a texture characteristic parameter data set of all sample points as independent variables, taking all sample points as training samples, and taking conductivity data actually measured by the training samples as dependent variables;
step 6, carrying out large-area remote sensing inversion on the soil conductivity
And determining a sliding window according to the row and column numbers corresponding to the soil sample point sampling areas, performing convolution calculation on unmanned aerial vehicle remote sensing images covering the whole measuring areas, extracting central pixels in each sliding window as prediction samples, and taking spectral characteristic parameter data sets and texture characteristic parameter data sets of all the prediction samples into a neural network prediction model to realize unmanned aerial vehicle remote sensing measurement of the salt content of the soda salinized soil.
Further, the step 1 specifically comprises the following steps:
the adopted equipment comprises a multi-rotor unmanned plane, a handheld GPS and a rectangular metal stator with the inner diameter of 1m multiplied by 1mThe standard frame, unmanned aerial vehicle installs CCD camera lens and hyperspectral imaging spectrometer of high definition, at first, uses online map platform to confirm a rectangle research area S that awaits measuring saline-alkali soil conductivity 0 Rectangular research area S is extracted according to online map platform 0 Is defined by four vertices P 01 、P 02 、P 03 、P 04 Longitude and latitude positions of (a) and a rectangular study area S 0 Then the whole rectangular investigation region S 0 Evenly divided into 4 rectangular subregions of equal size, and the side length L of each rectangular subregion 1 Wherein L is 1 =0.5l, while determining 4 rectangular sub-areas S according to a rank distribution 11 、S 12 、S 13 And S is 14 Each sub-area is used as an inscribed square of the circular area covered by the CCD lens, and the radius of the circular area is calculatedMeanwhile, according to the vertical shooting field angle A of the CCD lens, the fixed aerial shooting height of the unmanned aerial vehicle is calculated>According to four vertices P 01 、P 02 、P 03 And P 04 Determining the flying height H according to the longitude and latitude positions of the unmanned aerial vehicle and the boundary of each rectangular subarea, wherein the flying point of the unmanned aerial vehicle is a position point P 10 The position point is a rectangular subarea S 11 Left side edge intermediate position point, position point P 20 Is a rectangular subregion S 12 Right side edge intermediate position point, position point P 30 Is a rectangular subregion S 13 Left side edge intermediate position point, position point P 40 Is a rectangular subregion S 14 The middle position point of the right side is the flight end point; at the same time by the position point P 10 →P 20 →P 30 →P 40 As unmanned aerial vehicle flight route marking points, controlling the unmanned aerial vehicle to cover the whole rectangular research area S according to the flight path of the flight route marking points 0 Shooting of CCD high-definition remote sensing image and hyperspectral remote sensing image of unmanned aerial vehicle on soil surface are carried outThe method comprises the steps of carrying out a first treatment on the surface of the Because the lens coverage area is the circumscribed circle of the rectangular subarea when the CCD lens shoots, the area of the CCD high-definition unmanned aerial vehicle remote sensing image and the next shooting area are overlapped when shooting each time, and the CCD high-definition unmanned aerial vehicle remote sensing image is overlapped in each rectangular subarea S 11 、S 12 、S 13 And S is 14 The center point of (a) is taken as a sampling point P 1 、P 2 、P 3 And P 4 In the rectangular subarea S 11 And rectangular subareas S 12 Four sampling points P are arranged in the overlapping area of (1) 11 、P 12 、P 21 And P 22 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 12 And rectangular subareas S 14 Four sampling points P are arranged in the overlapping area of (1) 23 、P 24 、P 41 And P 42 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 14 And rectangular subareas S 13 Four sampling points P are arranged in the overlapping area of (1) 43 、P 44 、P 31 And P 32 The method comprises the steps of carrying out a first treatment on the surface of the Rectangular subregion S 13 And rectangular subareas S 11 Four sampling points P are arranged in the overlapping area of (1) 33 、P 34 、P 13 And P 14 Recording longitude and latitude positions of sampling points of each overlapping area, and placing rectangular metal calibration frames at each sampling point for geometric correction and splicing of subsequent unmanned aerial vehicle CCD high-definition remote sensing images and hyperspectral remote sensing images; and finally, after the unmanned aerial vehicle flies, collecting soil samples at the center point of the rectangular subarea and the sampling point of the overlapping area, drying the collected soil samples in a laboratory, preparing suspension with the water-soil mass ratio of 5:1, measuring the actual conductivity value of each soil sample in the laboratory, and storing all conductivity measurement results as a conductivity data set E to be used as a modeling sample set of a soil conductivity prediction model of the whole rectangular research area.
Further, the step 2 specifically comprises the following steps:
step 2.1, performing image mosaic, geometric correction and cutting on CCD high-definition remote sensing images of four rectangular subregions obtained by the unmanned aerial vehicle at a fixed height H: for rectangular subareas S 11 And rectangular subareas S 12 CCD high-definition remote sensing image for shootingImage i 1 And i 2 Splicing to generate a splicing result I 1 Then splice result I 1 And rectangular subareas S 14 Shot CCD high-definition remote sensing image i 3 Splicing to generate a splicing result I 2 Then splice result I 2 And rectangular subareas S 13 Shot CCD high-definition remote sensing image i 4 Splicing to generate a splicing result I 3 Finally, the splicing result I 3 Again, the image is combined with CCD high-definition remote sensing image i 1 Performing splice verification, if no ghost image exists, proving a splice result I 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole CCD high-definition remote sensing image;
for the splicing result I 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 02 、P 03 And P 04 GPS longitude and latitude measured data of (1), each rectangular subarea S 11 、S 12 、S 13 And S is 14 Center point P of (2) 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1) to the splicing result I 3 The corresponding position points on the surface are geometrically corrected based on polynomials, thereby realizing the whole rectangular research area S 0 Performing geometric distortion correction on all pixel points to generate a corrected remote sensing image I 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image I 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image I 4 Finally generates the whole rectangular research area S 0 The corresponding rectangular CCD high-definition remote sensing image map Z1;
step 2.2, performing image mosaic, geometric correction and cutting on hyperspectral remote sensing images of four rectangular subregions acquired by the unmanned aerial vehicle at a fixed height: for rectangular subareas S 11 And S is 12 Measured hyperspectral remote sensing image p 1 And p 2 Splicing to generate a splicing result Q 1 Then splice theResults Q 1 And rectangular subareas S 14 Measured hyperspectral remote sensing image p 3 Splicing to generate a splicing result Q 2 Then splice the result Q 2 And rectangular subareas S 13 Measured hyperspectral remote sensing image p 4 Splicing to generate a splicing result Q 3 Finally, the splicing result Q 3 Again with splice result Q 1 Performing splice verification, if the splice result Q 3 And splice result Q 1 No ghost phenomenon, proof of splice result Q 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole hyperspectral resolution remote sensing image; second, to splice result Q 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 0 、P 03 And P 04 GPS longitude and latitude measured data of each rectangular subarea and a central point P of each rectangular subarea 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1), to the splice result Q 3 The corresponding position points on the surface are geometrically corrected based on polynomials, thereby realizing the whole rectangular research area S 0 Performing geometric distortion correction on all pixel points on the remote sensing image to generate a corrected remote sensing image Q 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image Q 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image Q 4 Cutting the remote sensing image to finally generate the whole rectangular research area S 0 A corresponding rectangular hyperspectral remote sensing image map Z2;
further, the step 3 specifically comprises the following steps:
for 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 Calculating the correlation coefficient of the conductivity measured values of 20 sampling points and the reflectivity values of the sampling points in each wave band, drawing correlation coefficient curves of the 20 sampling points in all the wave bands, selecting 5 wave bands with the highest reflectivity and conductivity correlation as characteristic spectrum wave bands according to the correlation coefficient curves, wherein the wavelengths corresponding to the characteristic spectrum wave bands are lambda respectively 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The method comprises the steps of carrying out a first treatment on the surface of the Then, extracting each sampling point in the characteristic spectrum band lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The reflectivity of the position is stored as a data set T1 of the reflectivity, and the characteristic spectrum wave band lambda of each sampling point is calculated 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The first derivative of the reflectivity at the position is stored as a data set T2 of the first derivative of the reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The second derivative of the reflectivity at the point and saving as a data set T3 of the second derivative of the reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Log of reflectivity at the point and saving as a data set T4 of the log of reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Inverse of reflectivity at the point and saved as a data set T5 of inverse reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The square root of the reflectivity at and saved as the data set T6 of the square root of reflectivity; the datasets T1, T2, T3, T4, T5 and T6 were synthesized into a rectangular study area S 0 Spectral reflectance characteristic parameter data sets T of all soil sample points;
further, the step 4 specifically comprises the following steps:
according to rectangular investigation region S 0 Left boundary two vertices P 01 、P 03 Latitude of location W1, W3, and entire rectangular investigation region S 0 The corresponding line number H of the image map Z1 is calculated for each Actual size corresponding to each pixelSimultaneously calculating the number of pixels of the side length of the rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in the rectangular metal calibration frame>Then for 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 For the center, 20 rectangular CCD high-definition remote sensing sub-images B are correspondingly extracted and cut on an image map Z1 1 、B 2 、B 3 、B 4 、B 11 、B 12 、B 13 、B 14 、B 21 、B 22 、B 23 、B 24 、B 31 、B 32 、B 33 、B 34 、B 41 、B 42 、B 43 And B 44 Gray level processing is carried out on the clipping result image, 256 gray levels are calculated according to the processed gray level image, gray level co-occurrence matrixes M1, M2, M3 and M4 of rectangular CCD high-definition remote sensing sub-images corresponding to each sampling point on the steps of 0 DEG, 45 DEG, 90 DEG and 135 DEG are calculated, four-way average contrast texture feature quantities of rectangular CCD high-definition remote sensing sub-images corresponding to all the sampling points are calculated in each direction and stored as a data set C1, four-way average energy value texture feature quantities and stored as a data set C2, four-way average entropy texture feature quantities and stored as a data set C3 and four-way average consistency texture feature quantities and stored as a data set C4; synthesizing the data sets C1, C2, C3 and C4 into a crack characteristic parameter data set C of all soil sample points in the research area;
further, the step 5 specifically comprises the following steps:
the method comprises the steps of carrying out standardization processing on various spectral reflectance characteristic parameter data sets in a data set T by utilizing a neural network tool box of MATLAB software and a spectral reflectance characteristic parameter data set T and a crack characteristic parameter data set C of all sample points to generate a standardized spectral reflectance characteristic parameter data set T ', carrying out standardization processing on various soil crack characteristic parameter data sets in the data set C to generate a standardized crack characteristic parameter data set C', and realizing establishment of a soil conductivity prediction model; taking all 20 sampling points as training samples, taking a standardized spectral reflectance characteristic parameter data set T 'and a standardized crack characteristic parameter data set C' of the training samples as independent variables, taking a soil conductivity measurement data set E as dependent variables, establishing an artificial neural network prediction model, setting the iteration times k1=100, an error threshold k2=0.4 and an initial learning rate k3=0.2 of the neural network, and establishing a neural network prediction model of the soil conductivity on the basis, wherein the model form is E=f (T ', C'); further, the step 6 specifically includes:
According to the training sample points, namely the number U of side length pixels of a rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in a rectangular metal calibration frame of the sampling points 1 A rectangular sliding window is established, and the side length of the rectangular sliding window is U 1 The method comprises the steps of carrying out a first treatment on the surface of the Then, starting from the first pixel at the upper left corner of the image Z2 and the image Z1 respectively, covering the sliding window with the image Z1 and the image Z2, and traversing the whole image Z1 and the whole image Z2 element by element;
for the image Z2, each sliding pixel, according to step 3, the central pixel point in the sliding window is extracted in the spectrum band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 The reflectivity t1 at the position is calculated, and the central pixel point in the sliding window is positioned in the characteristic spectrum wave band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A first derivative t2 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 A second derivative t3 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Logarithm of reflectivity at t4; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Inverse of reflectivity at t5; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 The square root t6 of the reflectivity at the position, and combining the reflectivity t1, the first derivative t2, the second derivative t3, the logarithm t4, the reciprocal t5 and the square root t6 into a reflectivity characteristic parameter data set t (i 1, j 1) of the central pixel in the window, wherein i1 is the row number of the central pixel of the current sliding window in the image graph Z2, and j1 is the column number of the central pixel of the current sliding window in the image graph Z2;
For each pixel in the image Z1, respectively extracting four-direction average contrast texture feature quantity c1 of a central pixel of a sliding window, extracting four-direction average energy value texture feature quantity c2 of the central pixel of the sliding window, extracting four-direction average entropy value texture feature quantity c3 of the central pixel of the sliding window, extracting four-direction average consistency texture feature quantity c4 of the central pixel of the sliding window, and combining the contrast texture feature quantity c1, the energy value texture feature quantity c2, the entropy value texture feature quantity c3 and the consistency texture feature quantity c4 into a crack feature parameter data set c (i 2, j 2) of the central pixel in the window, wherein i2 is the row number of the central pixel of the current sliding window in the image Z1, and j2 is the column number of the central pixel of the current sliding window in the image Z1;
introducing a reflectivity characteristic parameter data set T (i 1, j 1) =T 'and a crack characteristic parameter data set C (i 2, j 2) =C' into an artificial neural network prediction model E=f (T ', C'), and calculating a conductivity prediction value of a central pixel of the sliding window; then traversing the whole image Z1 and the whole image Z2 by the method, and finally realizing the rectangular research area S 0 Remote sensing measurement of soil conductivity values in all pixels.
The invention has the outstanding beneficial effects and advantages that: compared with the traditional laboratory conductivity measurement and the field conductivity measurement by using a geodetic conductivity meter, the method has the characteristics of low cost, low manufacturing cost, small loss, capability of realizing real-time and synchronous observation of the conductivity of soil with a large area and strong maneuverability. According to the invention, the precipitation condition of the salt minerals on the soil surface can be objectively reflected, and the spectral characteristic parameters extracted according to the diagnostic spectral bands of the salt minerals are directly influenced by the soil salt, so that the parameter relation of the prediction model is clear, and the measurement precision is high.
Drawings
Fig. 1 is a schematic diagram of unmanned aerial vehicle geodetic measurement;
FIG. 2 is a schematic illustration of a flight trajectory and range of an unmanned aerial vehicle;
fig. 3 is a schematic diagram of a rectangular whole area, a rectangular sub-area and related position points of the investigation region.
Detailed Description
The invention is further illustrated by the following examples:
example 1
A method for measuring the conductivity of cracked saline-alkali soil based on unmanned aerial vehicle low-altitude remote sensing images comprises the following steps:
step 1, unmanned aerial vehicle remote sensing image acquisition
As shown in fig. 1-3, the device comprises a multi-rotor unmanned plane, a handheld GPS and a rectangular metal calibration frame with an inner diameter of 1m×1m, wherein the unmanned plane is provided with a high-definition CCD lens and a hyperspectral imaging spectrometer, and firstly, a rectangular research area S for measuring the conductivity of saline-alkali soil is determined by using an online map platform 0 Rectangular research area S is extracted according to online map platform 0 Is defined by four vertices P 01 、P 02 、P 03 、P 04 Longitude and latitude positions of (a) and a rectangular study area S 0 Then the whole rectangular investigation region S 0 Evenly divided into 4 rectangular subregions of equal size, and the side length L of each rectangular subregion 1 Wherein L is 1 =0.5l, while determining 4 rectangular sub-areas S according to a rank distribution 11 、S 12 、S 13 And S is 14 Each sub-area is used as an inscribed square of the circular area covered by the CCD lens, and the radius of the circular area is calculatedMeanwhile, according to the vertical shooting field angle A of the CCD lens, the fixed aerial shooting height of the unmanned aerial vehicle is calculated>According to four vertices P 01 、P 02 、P 03 And P 04 Determining the flying height H according to the longitude and latitude positions of the unmanned aerial vehicle and the boundary of each rectangular subarea, wherein the flying point of the unmanned aerial vehicle is a position point P 10 The position point is a rectangular subarea S 11 Left side edge intermediate position point, position point P 20 Is a rectangular subregion S 12 Right side edge intermediate position point, position point P 30 Is a rectangular subregion S 13 Left side edge intermediate position point, position point P 40 Is a rectangular subregion S 14 The middle position point of the right side is the flight end point; at the same time by the position point P 10 →P 20 →P 30 →P 40 As unmanned aerial vehicle flight route marking points, controlling the unmanned aerial vehicle to cover the whole rectangular research area S according to the flight path of the flight route marking points 0 Shooting a CCD high-definition remote sensing image of the unmanned aerial vehicle on the soil surface and shooting a hyperspectral remote sensing image; because the lens coverage area is the circumscribed circle of the rectangular subarea when the CCD lens shoots, the area of the CCD high-definition unmanned aerial vehicle remote sensing image and the next shooting area are overlapped when shooting each time, and the CCD high-definition unmanned aerial vehicle remote sensing image is overlapped in each rectangular subarea S 11 、S 12 、S 13 And S is 14 The center point of (a) is taken as a sampling point P 1 、P 2 、P 3 And P 4 In the rectangular subarea S 11 And rectangular subareas S 12 Four sampling points P are arranged in the overlapping area of (1) 11 、P 12 、P 21 And P 22 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 12 And rectangular subareas S 14 Four sampling points P are arranged in the overlapping area of (1) 23 、P 24 、P 41 And P 42 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 14 And rectangular subareas S 13 Four sampling points P are arranged in the overlapping area of (1) 43 、P 44 、P 31 And P 32 The method comprises the steps of carrying out a first treatment on the surface of the Rectangular subregion S 13 And rectangular subareas S 11 Four sampling points P are arranged in the overlapping area of (1) 33 、P 34 、P 13 And P 14 Recording longitude and latitude positions of sampling points of each overlapping area, and placing rectangular metal calibration frames at each sampling point for geometric correction and splicing of subsequent unmanned aerial vehicle CCD high-definition remote sensing images and hyperspectral remote sensing images; finally, after the unmanned aerial vehicle flies, collecting soil samples at the center point of the rectangular subarea and the sampling point of the overlapping area, drying the collected soil samples in a laboratory, preparing suspension with the water-soil mass ratio of 5:1, measuring the actual conductivity value of each soil sample in the laboratory, and storing all conductivity measurement results as a conductivity data set E to be used as a modeling sample set of a soil conductivity prediction model of the whole rectangular research area;
Step 2, preprocessing of unmanned aerial vehicle remote sensing images
Step 2.1, performing image mosaic, geometric correction and cutting on CCD high-definition remote sensing images of four rectangular subregions obtained by the unmanned aerial vehicle at a fixed height H: for rectangular subareas S 11 And rectangular subareas S 12 Shot CCD high-definition remote sensing image i 1 And i 2 Splicing to generate a splicing result I 1 Then splice result I 1 And rectangular subareas S 14 Shot CCD high-definition remote sensing image i 3 Splicing to generate a splicing result I 2 Then splice result I 2 And rectangular subareas S 13 Shot CCD high-definition remote sensing image i 4 Splicing to generate a splicing result I 3 Finally, the splicing result I 3 Again, the image is combined with CCD high-definition remote sensing image i 1 Performing splice verification, if no ghost image exists, proving a splice result I 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole CCD high-definition remote sensing image;
for the splicing result I 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 02 、P 03 And P 04 GPS longitude and latitude measured data of (1), each rectangular subarea S 11 、S 12 、S 13 And S is 14 Center point P of (2) 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1) to the splicing result I 3 The corresponding position points on the surface are geometrically corrected based on polynomials, thereby realizing the whole rectangular research area S 0 Performing geometric distortion correction on all pixel points to generate a corrected remote sensing image I 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image I 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image I 4 Finally generates the whole rectangular research area S 0 The corresponding rectangular CCD high-definition remote sensing image map Z1;
step 2.2, performing image mosaic, geometric correction and cutting on hyperspectral remote sensing images of four rectangular subregions acquired by the unmanned aerial vehicle at a fixed height: for rectangular subareas S 11 And S is 12 Measured hyperspectral remote sensing image p 1 And p 2 Splicing to generate a splicing result Q 1 Then splice the result Q 1 And rectangular subareas S 14 Measured hyperspectral remote sensing image p 3 Splicing to generate a splicing result Q 2 Then splice the result Q 2 And rectangular subareas S 13 Measured hyperspectral remote sensing image p 4 Splicing to generate a splicing result Q 3 Finally, the splicing result Q 3 Again with splice result Q 1 Performing splice verification, if the splice result Q 3 And splice result Q 1 No ghost phenomenon, proof of splice result Q 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole hyperspectral resolution remote sensing image; second, to splice result Q 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 0 、P 03 And P 04 GPS longitude and latitude measured data of (1), and eachCenter point P of rectangular subregion 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1), to the splice result Q 3 The corresponding position points on the surface are geometrically corrected based on polynomials, thereby realizing the whole rectangular research area S 0 Performing geometric distortion correction on all pixel points on the remote sensing image to generate a corrected remote sensing image Q 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image Q 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image Q 4 Cutting the remote sensing image to finally generate the whole rectangular research area S 0 A corresponding rectangular hyperspectral remote sensing image map Z2;
step 3, extracting spectral characteristic parameters
For 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 Calculating the correlation coefficient of the conductivity measured values of 20 sampling points and the reflectivity values of the sampling points in each wave band, drawing correlation coefficient curves of the 20 sampling points in all the wave bands, selecting 5 wave bands with the highest reflectivity and conductivity correlation as characteristic spectrum wave bands according to the correlation coefficient curves, wherein the wavelengths corresponding to the characteristic spectrum wave bands are lambda respectively 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The method comprises the steps of carrying out a first treatment on the surface of the Then, extracting each sampling point in the characteristic spectrum band lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The reflectivity of the position is stored as a data set T1 of the reflectivity, and the characteristic spectrum wave band lambda of each sampling point is calculated 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The first derivative of the reflectivity at the position is stored as a data set T2 of the first derivative of the reflectivity; calculating characteristic light of each sampling pointSpectral band lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The second derivative of the reflectivity at the point and saving as a data set T3 of the second derivative of the reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Log of reflectivity at the point and saving as a data set T4 of the log of reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Inverse of reflectivity at the point and saved as a data set T5 of inverse reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The square root of the reflectivity at and saved as the data set T6 of the square root of reflectivity; the datasets T1, T2, T3, T4, T5 and T6 were synthesized into a rectangular study area S 0 Spectral reflectance characteristic parameter data sets T of all soil sample points;
step 4, extracting characteristic parameters of soil surface
According to rectangular investigation region S 0 Left boundary two vertices P 01 、P 03 Latitude of location W1, W3, and entire rectangular investigation region S 0 The corresponding line number H of the image map Z1 calculates the actual size corresponding to each pixelSimultaneously calculating the number of pixels of the side length of the rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in the rectangular metal calibration frame>Then for 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 For the center, 20 rectangular CCD high-definition remote sensing sub-images are correspondingly extracted and cut on an image map Z1B 1 、B 2 、B 3 、B 4 、B 11 、B 12 、B 13 、B 14 、B 21 、B 22 、B 23 、B 24 、B 31 、B 32 、B 33 、B 34 、B 41 、B 42 、B 43 And B 44 Gray level processing is carried out on the clipping result image, 256 gray levels are calculated according to the processed gray level image, gray level co-occurrence matrixes M1, M2, M3 and M4 of rectangular CCD high-definition remote sensing sub-images corresponding to each sampling point on the steps of 0 DEG, 45 DEG, 90 DEG and 135 DEG are calculated, four-way average contrast texture feature quantities of rectangular CCD high-definition remote sensing sub-images corresponding to all the sampling points are calculated in each direction and stored as a data set C1, four-way average energy value texture feature quantities and stored as a data set C2, four-way average entropy texture feature quantities and stored as a data set C3 and four-way average consistency texture feature quantities and stored as a data set C4; synthesizing the data sets C1, C2, C3 and C4 into a crack characteristic parameter data set C of all soil sample points in the research area;
step 5, establishing a conductivity prediction model
The method comprises the steps of carrying out standardization processing on various spectral reflectance characteristic parameter data sets in a data set T by utilizing a neural network tool box of MATLAB software and a spectral reflectance characteristic parameter data set T and a crack characteristic parameter data set C of all sample points to generate a standardized spectral reflectance characteristic parameter data set T ', carrying out standardization processing on various soil crack characteristic parameter data sets in the data set C to generate a standardized crack characteristic parameter data set C', and realizing establishment of a soil conductivity prediction model; taking all 20 sampling points as training samples, taking a standardized spectral reflectance characteristic parameter data set T 'and a standardized crack characteristic parameter data set C' of the training samples as independent variables, taking a soil conductivity measurement data set E as dependent variables, establishing an artificial neural network prediction model, setting the iteration times k1=100, an error threshold k2=0.4 and an initial learning rate k3=0.2 of the neural network, and establishing a neural network prediction model of the soil conductivity on the basis, wherein the model form is E=f (T ', C');
Step 6, carrying out large-area remote sensing inversion on the soil conductivity
According to the training sample points, namely the number U of side length pixels of a rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in a rectangular metal calibration frame of the sampling points 1 A rectangular sliding window is established, and the side length of the rectangular sliding window is U 1 The method comprises the steps of carrying out a first treatment on the surface of the Then, starting from the first pixel at the upper left corner of the image Z2 and the image Z1 respectively, covering the sliding window with the image Z1 and the image Z2, and traversing the whole image Z1 and the whole image Z2 element by element;
for the image Z2, each sliding pixel, according to step 3, the central pixel point in the sliding window is extracted in the spectrum band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 The reflectivity t1 at the position is calculated, and the central pixel point in the sliding window is positioned in the characteristic spectrum wave band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A first derivative t2 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 A second derivative t3 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Logarithm of reflectivity at t4; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Inverse of reflectivity at t5; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 The square root t6 of the reflectivity at the position, and combining the reflectivity t1, the first derivative t2, the second derivative t3, the logarithm t4, the reciprocal t5 and the square root t6 into a reflectivity characteristic parameter data set t (i 1, j 1) of the central pixel in the window, wherein i1 is the row number of the central pixel of the current sliding window in the image graph Z2, and j1 is the column number of the central pixel of the current sliding window in the image graph Z2;
For each pixel in the image Z1, respectively extracting four-direction average contrast texture feature quantity c1 of a central pixel of a sliding window, extracting four-direction average energy value texture feature quantity c2 of the central pixel of the sliding window, extracting four-direction average entropy value texture feature quantity c3 of the central pixel of the sliding window, extracting four-direction average consistency texture feature quantity c4 of the central pixel of the sliding window, and combining the contrast texture feature quantity c1, the energy value texture feature quantity c2, the entropy value texture feature quantity c3 and the consistency texture feature quantity c4 into a crack feature parameter data set c (i 2, j 2) of the central pixel in the window, wherein i2 is the row number of the central pixel of the current sliding window in the image Z1, and j2 is the column number of the central pixel of the current sliding window in the image Z1;
introducing a reflectivity characteristic parameter data set T (i 1, j 1) =T 'and a crack characteristic parameter data set C (i 2, j 2) =C' into an artificial neural network prediction model E=f (T ', C'), and calculating a conductivity prediction value of a central pixel of the sliding window; then traversing the whole image Z1 and the whole image Z2 by the method, and finally realizing the research area S 0 And the soil conductivity values in all pixels are rapidly and synchronously measured remotely in a large area.
Claims (7)
1. The method for measuring the conductivity of the cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image is characterized by comprising the following steps of:
step 1, unmanned aerial vehicle remote sensing image acquisition
The method comprises the steps that the adopted equipment comprises a multi-rotor unmanned aerial vehicle, a handheld GPS and a rectangular metal calibration frame with the inner diameter of 1m multiplied by 1m, wherein the unmanned aerial vehicle is provided with a high-definition CCD lens and a hyperspectral imaging spectrometer, a rectangular research area is selected to be divided into four sub-areas for shooting of a CCD high-definition remote sensing image of the unmanned aerial vehicle on the soil surface and shooting of the hyperspectral remote sensing image, sampling points are selected for collecting soil samples, the conductivity true value of each soil sample is measured, and all conductivity measurement results are stored as a conductivity data set E to be used as a modeling sample set of a soil conductivity prediction model of the whole research area;
step 2, preprocessing of unmanned aerial vehicle remote sensing images
Performing image mosaic, geometric correction and cutting on the CCD high-definition remote sensing images shot by the four rectangular subregions to generate a rectangular CCD high-definition remote sensing image map Z1 corresponding to the whole rectangular research region; performing image mosaic, geometric correction and cutting on the hyperspectral remote sensing images shot by the four rectangular subregions to generate a rectangular hyperspectral remote sensing image map Z2 corresponding to the whole rectangular research region;
Step 3, extracting spectral characteristic parameters
Searching full-band hyperspectral reflectivity data of a sampling point according to position information of the sampling point, calculating a characteristic spectrum band of the sampling point on the basis, and further calculating first derivative, second derivative, logarithm, reciprocal and square root mathematical transformation parameters of the characteristic band spectrum reflectivity data as spectrum characteristic parameters to form a spectrum reflectivity characteristic parameter data set of the soil sample point;
step 4, extracting characteristic parameters of soil surface
Determining the row number of image pixels corresponding to a 1m multiplied by 1m earth surface area according to the position information of sampling points, taking each sample point as a center, determining the image size of the soil sample surface according to the earth surface area size, cutting all sample point data according to the size, carrying out graying treatment on a cutting result image, calculating 256 gray levels according to the treated gray level image, extracting a contrast ratio, an angular second moment, consistency and four statistical texture characteristic parameters according to the gray level symbiotic matrix, wherein the step length of the gray level symbiotic matrix is in the four directions of 0, 45, 90 and 135 degrees, and synthesizing the four statistical texture characteristic parameters into a crack characteristic parameter data set of all soil sample points in a research area;
Step 5, establishing a conductivity prediction model
The method comprises the steps of establishing a salinized soil conductivity neural network prediction model based on unmanned aerial vehicle remote sensing image data by using a neural network toolbox of computer MATLAB software, taking a spectral characteristic parameter data set T of all sample points and a texture characteristic parameter data set of all sample points as independent variables, taking all sample points as training samples, and taking conductivity data actually measured by the training samples as dependent variables;
step 6, carrying out large-area remote sensing inversion on the soil conductivity
And determining a sliding window according to the row and column numbers corresponding to the soil sample point sampling areas, performing convolution calculation on unmanned aerial vehicle remote sensing images covering the whole measuring areas, extracting central pixels in each sliding window as prediction samples, and taking spectral characteristic parameter data sets and texture characteristic parameter data sets of all the prediction samples into a neural network prediction model to realize unmanned aerial vehicle remote sensing measurement of the salt content of the soda salinized soil.
2. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 1, wherein the step 1 is specifically as follows:
the adopted equipment comprises a multi-rotor unmanned plane, a handheld GPS (global positioning system) and a rectangular metal calibration frame with the inner diameter of 1m multiplied by 1m, wherein the unmanned plane is provided with a high-definition CCD (charge coupled device) lens and a hyperspectral imaging spectrometer, and firstly, an on-line map platform is used for determining a rectangular research area S for measuring the conductivity of saline-alkali soil 0 Rectangular research area S is extracted according to online map platform 0 Is defined by four vertices P 01 、P 02 、P 03 、P 04 Longitude and latitude positions of (a) and a rectangular study area S 0 Then the whole rectangular investigation region S 0 Evenly divided into 4 rectangular subregions of equal size, and the side length L of each rectangular subregion 1 Wherein L is 1 =0.5l, while determining 4 rectangular sub-areas S according to a rank distribution 11 、S 12 、S 13 And S is 14 Each sub-area is used as an inscribed square of the circular area covered by the CCD lens, and the radius of the circular area is calculatedMeanwhile, according to the vertical shooting field angle A of the CCD lens, the fixed aerial shooting height of the unmanned aerial vehicle is calculated>According to four vertices P 01 、P 02 、P 03 And P 04 Is defined by the latitude and longitude positions of each rectangular subareaBoundary, determining flying height H, wherein the flying point of the unmanned plane is a position point P 10 The position point is a rectangular subarea S 11 Left side edge intermediate position point, position point P 20 Is a rectangular subregion S 12 Right side edge intermediate position point, position point P 30 Is a rectangular subregion S 13 Left side edge intermediate position point, position point P 40 Is a rectangular subregion S 14 The middle position point of the right side is the flight end point; at the same time by the position point P 10 →P 20 →P 30 →P 40 As unmanned aerial vehicle flight route marking points, controlling the unmanned aerial vehicle to cover the whole rectangular research area S according to the flight path of the flight route marking points 0 Shooting a CCD high-definition remote sensing image of the unmanned aerial vehicle on the soil surface and shooting a hyperspectral remote sensing image; because the lens coverage area is the circumscribed circle of the rectangular subarea when the CCD lens shoots, the area of the CCD high-definition unmanned aerial vehicle remote sensing image and the next shooting area are overlapped when shooting each time, and the CCD high-definition unmanned aerial vehicle remote sensing image is overlapped in each rectangular subarea S 11 、S 12 、S 13 And S is 14 The center point of (a) is taken as a sampling point P 1 、P 2 、P 3 And P 4 In the rectangular subarea S 11 And rectangular subareas S 12 Four sampling points P are arranged in the overlapping area of (1) 11 、P 12 、P 21 And P 22 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 12 And rectangular subareas S 14 Four sampling points P are arranged in the overlapping area of (1) 23 、P 24 、P 41 And P 42 The method comprises the steps of carrying out a first treatment on the surface of the In the rectangular subarea S 14 And rectangular subareas S 13 Four sampling points P are arranged in the overlapping area of (1) 43 、P 44 、P 31 And P 32 The method comprises the steps of carrying out a first treatment on the surface of the Rectangular subregion S 13 And rectangular subareas S 11 Four sampling points P are arranged in the overlapping area of (1) 33 、P 34 、P 13 And P 14 Recording longitude and latitude positions of sampling points of each overlapping area, and placing rectangular metal calibration frames at each sampling point for geometric correction and splicing of subsequent unmanned aerial vehicle CCD high-definition remote sensing images and hyperspectral remote sensing images; finally, after the unmanned aerial vehicle fliesAfter that, collecting soil samples at the center point of the rectangular subarea and the sampling point of the coincident area, drying the collected soil samples in a laboratory, preparing suspension with the water-soil mass ratio of 5:1, measuring the conductivity true value of each soil sample in the laboratory, and storing all the conductivity measurement results as a conductivity data set E to be used as a modeling sample set of a soil conductivity prediction model of the whole rectangular research area.
3. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 2, wherein the step 2 is specifically as follows:
step 2.1, performing image mosaic, geometric correction and cutting on CCD high-definition remote sensing images of four rectangular subregions obtained by the unmanned aerial vehicle at a fixed height H: for rectangular subareas S 11 And rectangular subareas S 12 Shot CCD high-definition remote sensing image i 1 And i 2 Splicing to generate a splicing result I 1 Then splice result I 1 And rectangular subareas S 14 Shot CCD high-definition remote sensing image i 3 Splicing to generate a splicing result I 2 Then splice result I 2 And rectangular subareas S 13 Shot CCD high-definition remote sensing image i 4 Splicing to generate a splicing result I 3 Finally, the splicing result I 3 Again, the image is combined with CCD high-definition remote sensing image i 1 Performing splice verification, if no ghost image exists, proving a splice result I 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole CCD high-definition remote sensing image;
for the splicing result I 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 02 、P 03 And P 04 GPS longitude and latitude measured data of (1), each rectangular subarea S 11 、S 12 、S 13 And S is 14 Center point P of (2) 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1) to the splicing result I 3 Corresponding bit onGeometric correction based on polynomial is carried out on the set point, thereby realizing the whole rectangular research area S 0 Performing geometric distortion correction on all pixel points to generate a corrected remote sensing image I 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image I 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image I 4 Finally generates the whole rectangular research area S 0 The corresponding rectangular CCD high-definition remote sensing image map Z1;
step 2.2, performing image mosaic, geometric correction and cutting on hyperspectral remote sensing images of four rectangular subregions acquired by the unmanned aerial vehicle at a fixed height: for rectangular subareas S 11 And S is 12 Measured hyperspectral remote sensing image p 1 And p 2 Splicing to generate a splicing result Q 1 Then splice the result Q 1 And rectangular subareas S 14 Measured hyperspectral remote sensing image p 3 Splicing to generate a splicing result Q 2 Then splice the result Q 2 And rectangular subareas S 13 Measured hyperspectral remote sensing image p 4 Splicing to generate a splicing result Q 3 Finally, the splicing result Q 3 Again with splice result Q 1 Performing splice verification, if the splice result Q 3 And splice result Q 1 No ghost phenomenon, proof of splice result Q 3 High precision and can be used as a final rectangular research area S 0 The corresponding whole hyperspectral resolution remote sensing image; second, to splice result Q 3 Geometric correction is performed by using the method in a rectangular investigation region S 0 Position point P at four vertexes of (2) 01 、P 0 、P 03 And P 04 GPS longitude and latitude measured data of each rectangular subarea and a central point P of each rectangular subarea 1 、P 2 、P 3 And P 4 GPS longitude and latitude actual measurement data of (1), to the splice result Q 3 The corresponding position points on the surface are geometrically corrected based on polynomials, thereby realizing the whole rectangular research area S 0 All pixel points on the remote sensing imageGeometric distortion correction is carried out, and a corrected remote sensing image Q is generated 4 The method comprises the steps of carrying out a first treatment on the surface of the Finally, in ARCGIS software, according to the entire rectangular investigation region S 0 Generating a vector rectangular boundary ROI by four vertexes of the image, and combining the vector rectangular boundary ROI with the remote sensing image Q 4 Overlapping, reserving all pixel points in the vector rectangular boundary ROI, and realizing remote sensing image Q 4 Cutting the remote sensing image to finally generate the whole rectangular research area S 0 And a corresponding rectangular hyperspectral remote sensing image Z2.
4. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 3, wherein the step 3 is specifically as follows:
for 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 Calculating the correlation coefficient of the conductivity measured values of 20 sampling points and the reflectivity values of the sampling points in each wave band, drawing correlation coefficient curves of the 20 sampling points in all the wave bands, selecting 5 wave bands with the highest reflectivity and conductivity correlation as characteristic spectrum wave bands according to the correlation coefficient curves, wherein the wavelengths corresponding to the characteristic spectrum wave bands are lambda respectively 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The method comprises the steps of carrying out a first treatment on the surface of the Then, extracting each sampling point in the characteristic spectrum band lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The reflectivity of the position is stored as a data set T1 of the reflectivity, and the characteristic spectrum wave band lambda of each sampling point is calculated 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The first derivative of the reflectivity at the position is stored as a data set T2 of the first derivative of the reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Second derivative of reflectivity at and protectA data set T3 stored as a second derivative of reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Log of reflectivity at the point and saving as a data set T4 of the log of reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 Inverse of reflectivity at the point and saved as a data set T5 of inverse reflectivity; calculating lambda of each sampling point in characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The square root of the reflectivity at and saved as the data set T6 of the square root of reflectivity; the datasets T1, T2, T3, T4, T5 and T6 were synthesized into a rectangular study area S 0 Spectral reflectance characteristic parameter data set T for all soil sample points.
5. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 4, wherein the step 4 is specifically as follows:
according to rectangular investigation region S 0 Left boundary two vertices P 01 、P 03 Latitude of location W1, W3, and entire rectangular investigation region S 0 The corresponding line number H of the image map Z1 calculates the actual size corresponding to each pixelSimultaneously calculating the number of pixels of the side length of the rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in the rectangular metal calibration frame>Then for 20 sampling points P 1 、P 2 、P 3 、P 4 、P 11 、P 12 、P 13 、P 14 、P 21 、P 22 、P 23 、P 24 、P 31 、P 32 、P 33 、P 34 、P 41 、P 42 、P 43 And P 44 For the center, correspondingly extract and cut 20 on the image map Z1Rectangular CCD high-definition remote sensing sub-image B 1 、B 2 、B 3 、B 4 、B 11 、B 12 、B 13 、B 14 、B 21 、B 22 、B 23 、B 24 、B 31 、B 32 、B 33 、B 34 、B 41 、B 42 、B 43 And B 44 Gray level processing is carried out on the clipping result image, 256 gray levels are calculated according to the processed gray level image, gray level co-occurrence matrixes M1, M2, M3 and M4 of rectangular CCD high-definition remote sensing sub-images corresponding to each sampling point on the steps of 0 DEG, 45 DEG, 90 DEG and 135 DEG are calculated, four-way average contrast texture feature quantities of rectangular CCD high-definition remote sensing sub-images corresponding to all the sampling points are calculated in each direction and stored as a data set C1, four-way average energy value texture feature quantities and stored as a data set C2, four-way average entropy texture feature quantities and stored as a data set C3 and four-way average consistency texture feature quantities and stored as a data set C4; the data sets C1, C2, C3 and C4 were synthesized as a crack signature parameter data set C for all soil sample points within the study area.
6. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 5, wherein the step 5 is specifically as follows:
the method comprises the steps of carrying out standardization processing on various spectral reflectance characteristic parameter data sets in a data set T by utilizing a neural network tool box of MATLAB software and a spectral reflectance characteristic parameter data set T and a crack characteristic parameter data set C of all sample points to generate a standardized spectral reflectance characteristic parameter data set T ', carrying out standardization processing on various soil crack characteristic parameter data sets in the data set C to generate a standardized crack characteristic parameter data set C', and realizing establishment of a soil conductivity prediction model; taking all 20 sampling points as training samples, taking a normalized spectral reflectance characteristic parameter data set T 'and a normalized crack characteristic parameter data set C' of the training samples as independent variables, taking a soil conductivity measurement data set E as a dependent variable, establishing an artificial neural network prediction model, setting the iteration times k1 = 100, an error threshold k2 = 0.4 and an initial learning rate k3 = 0.2 of the neural network, and establishing a neural network prediction model of the soil conductivity on the basis of the parameters, wherein the model form is E = f (T ', C').
7. The method for measuring the conductivity of cracked saline-alkali soil based on the unmanned aerial vehicle low-altitude remote sensing image according to claim 6, wherein the step 6 is specifically as follows:
according to the training sample points, namely the number U of side length pixels of a rectangular image area corresponding to the inner diameter of 1m multiplied by 1m in a rectangular metal calibration frame of the sampling points 1 A rectangular sliding window is established, and the side length of the rectangular sliding window is U 1 The method comprises the steps of carrying out a first treatment on the surface of the Then, starting from the first pixel at the upper left corner of the image Z2 and the image Z1 respectively, covering the sliding window with the image Z1 and the image Z2, and traversing the whole image Z1 and the whole image Z2 element by element;
for the image Z2, each sliding pixel, according to step 3, the central pixel point in the sliding window is extracted in the spectrum band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 The reflectivity t1 at the position is calculated, and the central pixel point in the sliding window is positioned in the characteristic spectrum wave band lambda 1 、λ 2 、λ 3 、λ 4 、λ 5 A first derivative t2 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 A second derivative t3 of the reflectivity at; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Logarithm of reflectivity at t4; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 Inverse of reflectivity at t5; calculating the lambda of the central pixel point in the sliding window in the characteristic spectrum band 1 、λ 2 、λ 3 、λ 4 、λ 5 The square root t6 of the reflectivity at the point, the reflectivity t1, the first derivative t2, the second derivative t3, the logarithm t4, the reciprocal t5 and a square root t6 are combined to form a reflectivity characteristic parameter data set t (i 1, j 1) of a central pixel in the window, wherein i1 is a row number of the central pixel of the current sliding window in the image graph Z2, and j1 is a column number of the central pixel of the current sliding window in the image graph Z2;
for each pixel in the image Z1, respectively extracting four-direction average contrast texture feature quantity c1 of a central pixel of a sliding window, extracting four-direction average energy value texture feature quantity c2 of the central pixel of the sliding window, extracting four-direction average entropy value texture feature quantity c3 of the central pixel of the sliding window, extracting four-direction average consistency texture feature quantity c4 of the central pixel of the sliding window, and combining the contrast texture feature quantity c1, the energy value texture feature quantity c2, the entropy value texture feature quantity c3 and the consistency texture feature quantity c4 into a crack feature parameter data set c (i 2, j 2) of the central pixel in the window, wherein i2 is the row number of the central pixel of the current sliding window in the image Z1, and j2 is the column number of the central pixel of the current sliding window in the image Z1;
introducing a reflectivity characteristic parameter data set T (i 1, j 1) =T 'and a crack characteristic parameter data set C (i 2, j 2) =C' into an artificial neural network prediction model E=f (T ', C'), and calculating a conductivity prediction value of a central pixel of the sliding window; then traversing the whole image Z1 and the whole image Z2 by the method, and finally realizing the rectangular research area S 0 Remote sensing measurement of soil conductivity values in all pixels.
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| CN118172242A (en) * | 2024-05-13 | 2024-06-11 | 青岛国测海遥信息技术有限公司 | Unmanned plane water surface image stitching method, medium and system |
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| CN118172242A (en) * | 2024-05-13 | 2024-06-11 | 青岛国测海遥信息技术有限公司 | Unmanned plane water surface image stitching method, medium and system |
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