CN114418911B - Method for reducing scale and improving water body definition through statistical regression of remote sensing images - Google Patents
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Abstract
The invention discloses a method for reducing the scale of statistical regression of a remote sensing image and improving the definition of a water body, which comprises the following steps: s101, calculating an image statistical regression relation, and establishing a relation between two kinds of data with different resolutions, so that coarse resolution data can obtain a data value with fine resolution through a calculation formula; s102, performing image downscaling; s103, calculating river flow; s104, evaluating the water body definition: and calculating the relative river length ratio and the water continuity value in the river channel to evaluate the water definition in the image. The invention can improve the water body definition of the partial area affected by the cloud in the image; the data quantity is not reduced; after the method is applied, the original resolution of the remote sensing image data with thicker resolution can be improved, and the result can be mutually complemented with the fine resolution remote sensing image data, so that the respective advantages of the two data are maintained.
Description
Technical Field
The invention relates to a method for reducing the scale of statistical regression of a remote sensing image and improving the definition of a water body, relates to the field of remote sensing image processing, and particularly relates to the technical field of combination of water body identification indexes, remote sensing calculation of river flow, reduction of the scale of statistical regression and the like.
Background
The method for obtaining river flow data by using remote sensing images is a very popular means in current scientific research and production, and has the characteristics of rapidness, convenience and low cost. Since the 1996 scholars proposed normalized water index (NDWI), many studies and work have been developed around accurately measuring the water distribution of the surface. In the past, a great deal of work has focused on calculating the distribution of water by measuring indirect parameters, and more research has focused on directly measuring the water volume, flow rate and the like in a river channel, and the information representing the water body can be accurately separated by carrying out the operation of spectral information on remote sensing images obtained by satellites, thereby helping to determine water information data. Satellites with different attributes currently have different obtained data, the minimum resolvable unit of each satellite is expressed by spatial resolution, and satellite data with higher spatial resolution is clearer, so that the data volume obtained by sweeping the same place in one year is more, but the time period of rescanning the same place is longer. Therefore, the high-resolution data revisit period is long, the low-resolution data revisit period is short, the advantages and disadvantages of different data are different, and the data can be combined for use, so that the data use efficiency can be improved.
Meanwhile, most satellites on the ground mainly depend on light reflection, namely electromagnetic waves, are very easy to be interfered by clouds, fog and the like in an atmosphere, data loss or poor image viewing occur, and numerical value differences among different ground objects are not large, so that the use efficiency of the data is also affected. A computer algorithm program can be used to reduce the effect of the cloud, which is called a cloud removal algorithm, but it works well for thick clouds and poorly for thin clouds.
Generally, the cloud and fog mainly appear in tropical and subtropical areas, such as Yun Guigao original areas, and the cloud and fog are unevenly distributed and are sometimes shielded and sometimes not shielded, so that the use of one remote sensing image by light can lead to insufficient data, and various images can be mutually complemented, so that the advantages of various data are utilized. There is a regression method in the field of statistics, called statistical regression downscaling, that can combine multiple data, retaining their own advantages, such as high definition of high spatial resolution data, and high data volume of geospatial data. By means of the method, a larger number of low-spatial resolution images can be improved in definition, and therefore data utilization efficiency is improved.
The current technical method capable of reducing the influence of cloud and fog on the image definition can be divided into: (1) single image: the spectral information on the image affected by the cloud layer is acquired through a cloud removing algorithm, the spectral characteristics of the cloud layer are screened out, or a filtering method is directly adopted to remove specific wave bands in the spectrum, so that the remote sensing image or the NDWI image is calculated and processed, and the influence of the cloud layer is reduced; (2) a plurality of images: and extracting the part without cloud from different remote sensing images at the same place by utilizing the plurality of images, and jointly called an image without cloud. NDWI is the normalized water index: in 1996, MCFEETERS proposed a normalized water index (NDWI) which can accurately and effectively obtain water distribution information in a remote sensing image, such as water area, average river width, river length, etc., so as to be used for remote sensing calculation of river flow. The principle is that the difference of the spectral characteristics of the water body and other ground objects can be highlighted by carrying out linear operation on the green wave band and the near infrared wave band of the satellite image, so that the water body can be identified in the image more easily. ' s of
However, the above method still has the following problems: (1) The cloud removing algorithm can solve the problem that the definition of the image with partial cloud layer distribution cannot be well improved when the cloud and fog exist in the whole image or the influence of the cloud and fog on the whole image is relatively close; (2) The plurality of images can only obtain one image result, so that the result data is reduced for the purposes of identifying river water bodies and the like, and the development of further work is not facilitated; (3) Although the influence of cloud and fog on the removed images is reduced, the images are used independently, and the respective advantages of different data are not reasonably utilized.
The concepts and related technologies involved in the present invention are briefly summarized as follows:
Statistical regression: by establishing a statistical relationship between two different sets of data to obtain a mathematical formula between the two sets of data, a certain numerical value in one set of data can be input to obtain a corresponding numerical value in the other set of data.
Geometric relationship of water conservancy: according to the shape of a river in practice, the river is simulated by using a mathematical function, and the relationship among physical quantities such as river width, water depth, flow and the like is obtained by simulation.
Disclosure of Invention
The invention aims to provide a method for reducing the scale of a remote sensing image by statistical regression and improving the definition of a water body, so as to solve the problems of insufficient definition caused by the influence of cloud and fog and the like on a large number of remote sensing images at present, and the problems of insufficient quantity, low spatial resolution and the like of the images, and the problems of insufficient data sources when the images are used for identifying and calculating the surface water body caused by the fact that the data are not mutually complemented, so that the advantages of the images are fully exerted.
The invention aims to provide a method for reducing the scale of a remote sensing image by statistical regression and improving the definition of a water body, which is characterized in that a statistical regression relation is constructed between two remote sensing images with different spatial resolutions, the remote sensing images with coarse spatial resolutions are substituted into calculation by means of the constructed relation, the image with new fine spatial resolution is obtained by calculation, the original image with fine resolution is supplemented, the definition of the data which is more seriously influenced by cloud and fog is improved according to different degrees of the two data influenced by cloud and fog, and the recognition degree of the water body is improved.
Another object of the present invention is to provide a method for calculating a river flow in a remote sensing image based on the obtained image with fine spatial resolution.
The method for reducing the scale of the statistical regression of the remote sensing image and improving the definition of the water body specifically comprises the following steps:
s101, calculating image statistical regression relation
The purpose of this step is to: a relation is established between two kinds of data with different resolutions, so that coarse resolution data can obtain a data value with fine resolution through a calculation formula. The specific process is as follows:
NDWI data were read at two different resolutions: 1) The method comprises the steps of (1) forming a pair of data by one-to-one correspondence between each data a 'in the coarse resolution data set A and each data B' with the same time attribute in the fine resolution data set B; 2) Decomposing each large lattice point of each data in the coarse resolution data set A into small lattice points with the same size as the small lattice points in the fine resolution data set B according to the resolution of the fine resolution data set B, and assigning the values of the large lattice points of the original coarse resolution data set A to the decomposed small lattice points; 3) Each new pair of data a corresponds to the data b lattice point one by one. The linear correlation coefficient Coe between the two functions is simulated by means of a statistical linear regression function (a specific calculation formula is shown in formulas (1) to (3)), and the residual data Res is obtained by multiplying the data a and the Coe and then subtracting the data b.
Obji,j＝Coei,j×Orii,j+Resi,j (1)
Resi,j＝Obji,j-Coei,j×Orii,j (2)
Regi,j＝Coei,j×Orii,j+Resi,j (3)
Where Obj is data b, ori is data a, coe is a correlation coefficient of data b with respect to data a, res is a residual image, reg is a regressed image, and i, j correspond to row and column numbers of grid points in the image, respectively.
S102, image downscaling: the purpose of this step is to: by means of the relation between the coarse resolution data and the fine resolution data determined in the step S101, the same coarse resolution data is substituted into a calculation formula, so that corresponding fine resolution data can be obtained, namely, the remote sensing image data with the coarse resolution are processed into fine resolution data, so that the richness of the data in the same area is improved, and further calculation is facilitated. The specific process is as follows:
According to the statistical regression relation calculation program of step S101, each data of the coarse resolution data set C needing to be downscaled is processed into a new fine resolution data set D corresponding to the coarse resolution data set C needing to be downscaled after being substituted into the formula (1), the numerical values on all grid points are reckoned for the data in the new fine resolution data set D, and the obtained quotient is taken as the new numerical value of each grid point, so that the result normalization processing is facilitated, and the calculation and the checking of the result are facilitated, wherein the specific normalization formula is as follows:
Wherein P is a new value of the grid point, V is an original value of the grid point, V ave is an average value of all grid point image values in each data, V max is a maximum value in the statistical values, and V min is a minimum value in the statistical values.
The method of the invention further comprises the steps of:
S103, river flow calculation: the purpose of this step is to: and constructing a river simulation form and a flow calculation formula by using the existing remote sensing flow calculation principle, and completing the calculation process from the water surface area to the river flow. By means of the existing remote sensing flow measurement calculation method, digital surface model data, measured water depth and slope drop data obtained by unmanned aerial vehicle measurement are input, a hydraulic geometrical relationship is constructed, roughness and flow velocity data are input, and the river section shape can be obtained through fitting. Acquiring historical remote sensing images of sites through a Google Earth engine computing platform (GEE, https:// earthenine. Google. Com /) and computing to obtain NDWI image values, selecting water bodies and land training areas from the images, acquiring water body thresholds in batches, acquiring grid points and distribution areas representing the water bodies in a river channel in the training areas, carrying out statistical computation to obtain the width of the water bodies in the river channel, carrying out water conservancy geometrical relations, and computing to obtain the river channel flow values.
S104, evaluating water body definition
The following two values are calculated to evaluate the sharpness of the body of water in the image: ① And selecting a river range by using a high-precision low-altitude image generated by shooting of the unmanned aerial vehicle at a detection site, extracting a water NDWI threshold value learned by a training area, counting the number of water pixels in the range, calculating to obtain the water area in the river range, extracting the average river width from the river range, dividing the average river width by the water area to obtain the river water length, and calculating the relative river length occupation ratio L of the river water length in the whole river length. ② The water continuity value C in the river channel is obtained by counting the number of water bodies in the river channel, dividing the water body by the total water body area and making a difference value with 1, and the specific calculation formula is as follows:
Li＝Ai÷(RWii×Li) (6)
Wherein i is a corresponding river serial number, N i represents a continuous water plaque number of the i river, A i represents a total grid number of the water of the i river, RW i represents a river width, and LH i represents an overall length of the river.
After the four steps are sequentially carried out, the remote sensing image data with coarse resolution can be processed into the data with fine resolution, and the flow data in the river channel in the image can be obtained through calculation.
The invention has the beneficial effects that:
According to the method, the statistical regression relation between the two remote sensing image data is established, the value of the other data in the corresponding area can be calculated by means of the data less influenced by cloud and fog, and the definition of the partial area influenced by the cloud and fog in the image is improved; the images are in one-to-one correspondence to obtain a statistical regression relation, and each remote sensing image to be processed can obtain a corresponding result after the method is applied, so that the data quantity is not reduced; after the method is applied, the original resolution of the remote sensing image data with thicker resolution can be improved, and the result can be mutually complemented with the fine resolution remote sensing image data, so that the respective advantages of the two data are maintained.
Drawings
FIG. 1 is a schematic flow chart of the method of the invention
FIG. 2a, b shows the water definition before and after river use of the method of the present invention, a is the original image, b is the image after calculation;
FIG. 3a, b, the definition of the water before and after river two uses the method of the invention, a is the original image, b calculates the processed image;
fig. 4a and b show the water definition before and after the river three uses the method of the invention, a is the original image, b is the image after calculation treatment;
FIG. 5a, b, river four, before and after using the method of the invention, the water definition condition, a is the original image, b is the image after calculating the treatment;
FIG. 6a, b, the definition of the water before and after the fifth river using the method of the invention, a is the original image, b is the image after calculation;
FIG. 7a, b, river six, before and after using the method of the invention, the water definition condition, a is the original image, b is the image after calculating the treatment;
FIG. 8a, b, the water definition before and after using the method of the invention, a is the original image, b is the image after calculating the treatment;
FIG. 9a is the above-described water continuity for 7 rivers;
Fig. 9b shows the relative river length ratio of the 7 rivers.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present invention, numerous technical details have been set forth in order to provide a better understanding of the present invention. The claimed invention may be practiced without these specific details and with various changes and modifications based on the following embodiments.
A method for reducing the scale of a remote sensing image by statistical regression and improving the definition of a water body specifically comprises the following steps:
s101, calculating an image statistical regression relation: the invention carries out statistical regression downscaling on two types of remote sensing data such as NDWI with different spatial resolutions: the system comprises a plurality of remote sensing image pictures, a plurality of remote sensing image data sets and a plurality of sub-resolution data sets, wherein the sub-resolution data sets are divided into a coarse resolution data set A and a sub-resolution data set B according to the spatial resolution. Firstly, images a1', a2', a3 'to an' in A are sequentially selected according to time sequence, data B1', B2', B3 'to bn' with the same time attribute as the images are selected from B, a1', B1' form 1 st group data, a2', B2' form 2 nd group data, and an 'and bn' form n th group data. And then resampling the data a1 'to an' from A in each group of data in space, sampling the space minimum unit of the data set B with the small resolution of the lattice point size, and obtaining new data a1 to an. The following is then performed for each set of data: and sequentially selecting image values in corresponding grid points of the two remote sensing images a and b from the first grid point of the first row to the last grid point of the last row according to the space distribution sequence, respectively marking the grid point value from the image a as Ori i,j, and marking the grid point value from the image b as Obj i,j, wherein i and j respectively represent a row serial number and a column serial number. For each lattice point, one Ori value and one Obj value can be obtained in each data group, n Ori values and n Obj values can be obtained by n groups of data together, the n corresponding Ori values and n Obj values are brought into a linear regression formula to form an n-dimensional equation set, so that regression parameters Coe i,j and residual parameters Res i,j of the equation set can be obtained by calculation, and each lattice point can obtain respective regression parameters and residual parameters, and specific regression equations are shown in the following formulas (1) - (2). According to the regression parameters and residual parameters of each lattice point, the regression parameters and residual parameters can be brought into a downscaling equation to form a statistical regression downscaling calculation formula, and i multiplied by j calculation formulas can be obtained according to the lattice points, wherein the downscaling equation is shown in a formula (3) below.
Obji,j＝Coei,j×Orii,j+Resi,j (1)
Resi,j＝Obji,j-Coei,j×Orii,j (2)
Regi,j＝Coei,j×Orii,j+Resi,j (3)
Where Obj is data b, ori is data a, coe is a correlation coefficient of data b with respect to data a, res is a residual image, reg is a regressed image, and i, j correspond to row and column numbers of grid points in the image, respectively.
S102, image downscaling: after the statistical regression downscaling formula is established, each grid point in the image has a corresponding formula, and a new image value can be obtained by substituting the image value to be calculated into the formula. And selecting a remote sensing image data set C with unclear images and blurred water, and sequentially selecting images C1 and C2 in the remote sensing image data set C according to time sequence until cn. Each image is resampled as in step S101 to the spatial minimum unit of the fine resolution image. And then substituting the image values of the resampled images into formulas corresponding to the grid points according to the sequence of each point to obtain new image values, and calculating the new images D1, D2 and dn according to the time sequence to form a downscaled data set D. The image values of all grid points in each image in the new data set D are counted, the average value V ave, the maximum value V max and the minimum value V min of the image values are calculated, the numerical value of each grid point is substituted into a formula through a normalization formula to obtain a normalization result, the numerical distribution is more average, and a specific normalization formula is shown in a formula (4) below.
Wherein P is a new value of the grid point, V is an original value of the grid point, V ave is an average value of all grid point image values in each data, V max is a maximum value in each data, and V min is a minimum value in each data.
S103, river flow calculation: and acquiring river related parameters by means of a normalized fine resolution new remote sensing image data set E and adopting a remote sensing image flow measurement method. And determining the range of the river and the land in the unmanned aerial vehicle image by utilizing the high-precision unmanned aerial vehicle image which is measured in advance and is consistent with the spatial range of the remote sensing image in the data set, and determining the range as water and land. The river range water and the land range land are superimposed on the data set E, and the area where the river and the land should exist is selected by a frame to read the average image values V land and V water corresponding to the land and the river in the image, and the distribution area and the average width rivwidth of the river in the image are extracted through the image threshold values of the river and the land of V land and V water. The parameters of water depth, flow speed, roughness and gradient of the corresponding area obtained by actual measurement in advance are brought into a Manning formula of a hydraulic flow calculation formula, a formula (5) is shown below, and the relation between river flow and river width is constructed by measuring a known flow result Q i on the same day and RIVERWIDTH i obtained by clear unmanned plane image measurement. And finally, sequentially extracting the corresponding water widths RIVERWIDTH 1 to RIVERWIDTH n from each image E1, E2 to en in the data set E, taking the water widths into the constructed river width-flow relation, and calculating to obtain a flow value Q corresponding to each image.
Where k is the conversion factor, where 1, n is the roughness, A is the water cross-sectional area, P is the wet cycle length, and S is the hydraulic slope, where river slope is used instead.
S104, evaluating water body definition
The following two values are calculated to evaluate the sharpness of the body of water in the image: ① And selecting a river range by using a high-precision low-altitude image generated by shooting of the unmanned aerial vehicle at a detection site, extracting a water NDWI threshold value learned by a training area, counting the number of water pixels in the range, calculating to obtain the water area in the river range, extracting the average river width from the river range, dividing the average river width by the water area to obtain the river water length, and calculating the relative river length occupation ratio L of the river water length in the whole river length. ② The water continuity value C in the river channel is obtained by counting the number of water bodies in the river channel, dividing the water body by the total water body area and making a difference value with 1, and the specific calculation formula is as follows:
Li＝Ai÷(RWi×LHi) (6)
Wherein i is a corresponding river serial number, N i represents a continuous water plaque number of the i river, A i represents a total grid number of the water of the i river, RW i represents a river width, and LH i represents an overall length of the river.
Figures 2a, b to 8a, b are the water clarity before and after 7 rivers named 0501, 0502, 0401, 0402, 0404, 0405, 0406 use the method of the present invention; the graph a in each group represents an original image, the graph b represents an image after calculation processing, the light color in a river channel is the identified water body range, the dark color in the river channel is the land range, L and C in the graph respectively represent the relative river length ratio and the water body continuity, and the distribution condition of the water body can be represented; fig. 9a represents the water continuity of 7 rivers and fig. 9b represents the relative river length ratio of 7 rivers. Wherein, the dark bars represent the water body continuity index and relative river length ratio obtained in the original image, and the light bars represent the corresponding result obtained by the image after the method is applied.
Claims (1)
1. A method for reducing the scale of a remote sensing image by statistical regression and improving the definition of a water body is characterized by comprising the following steps of: the method specifically comprises the following steps:
s101, calculating image statistical regression relation
Establishing a relation between two kinds of data with different resolutions, so that coarse resolution data can obtain a data value with fine resolution through a calculation formula; the specific process is as follows:
NDWI data were read at two different resolutions: 1) The method comprises the steps of (1) forming a pair of data by one-to-one correspondence between each data a 'in the coarse resolution data set A and each data B' with the same time attribute in the fine resolution data set B; 2) Decomposing each large lattice point of each data in the coarse resolution data set A into small lattice points with the same size as the small lattice points in the fine resolution data set B according to the resolution of the fine resolution data set B, and assigning the values of the large lattice points of the original coarse resolution data set A to the decomposed small lattice points; 3) Corresponding each new pair of data a and data b in a one-to-one mode; simulating a linear correlation coefficient Coe between the two functions by means of a statistical linear regression function, namely formulas (1) to (3), multiplying the data a and the Coe, and then subtracting the data b to obtain residual data Res;
Obji,j＝Coei,j×Orii,j+Resi,j (1)
Resi,j＝Obji,j-Coei,j×Orii,j (2)
Regi,j＝Coei,j×Orii,j+Resi,j (3)
Where Obj is data b, ori is data a, coe is a correlation coefficient of the data b with respect to the data a, res is a residual image, reg is a regressed image, and i, j correspond to row and column numbers of grid points in the image respectively;
s102, image downscaling: according to the relationship between the coarse resolution data and the fine resolution data determined in the step S101, each data of the coarse resolution data set C needing to be downscaled is processed into a new fine resolution data set D corresponding to the coarse resolution data set C needing to be downscaled after being substituted into a formula (1) after being unified in resolution, the data in the new fine resolution data set D are reckoned with values on all grid points, and the obtained quotient is taken as a new value of each grid point to be normalized, so that the calculation and the checking of results are facilitated, and the specific normalization formula is as follows:
Wherein P is a new value of the grid point, V is an original value of the grid point, V ave is an average value of all grid point image values in each data, V max is a maximum value in the statistical values, and V min is a minimum value in the statistical values;
S103, river flow calculation:
constructing a river simulation form and a flow calculation formula by using the existing remote sensing flow calculation principle, and completing the calculation process from the water surface area to the river flow;
Inputting digital surface model data, measured water depth and slope data obtained by unmanned aerial vehicle measurement by means of the existing remote sensing flow measurement calculation method, constructing a water conservancy geometric relationship, inputting roughness and flow velocity data, and fitting to obtain a river section shape;
Acquiring historical remote sensing images of sites through a Google earth engine computing platform and computing the historical remote sensing images to form NDWI image values, selecting water body and land training areas from the images, acquiring water body threshold values in batches, acquiring grid points and distribution areas representing the water body in a river channel in the training areas, carrying out statistical computation to obtain the width of the water body in the river channel, carrying out the geometric relationship of water conservancy, and computing to obtain a river channel flow value;
s104, evaluating water body definition
The following two values are calculated to evaluate the sharpness of the body of water in the image:
① Selecting a river range through a high-precision low-altitude image generated by shooting of the unmanned aerial vehicle at a detection site, extracting a water NDWI threshold value learned by a training area, counting the number of water pixels in the range, calculating to obtain the water area in the river range, extracting an average river width from the river range, dividing the average river width by the water area to obtain the river water length, and calculating the relative river length occupation ratio L of the river water length in the whole river length;
② Calculating to obtain a water continuity value C in the river by counting the number of continuous water plaques in the river and dividing the number by the total water area and making a difference value with 1;
the specific calculation formula is as follows:
Li＝Ai÷(RWi×LHi) (6)
Wherein i is a corresponding river serial number, N i represents a continuous water plaque number of the i river, A i represents a total grid number of the water of the i river, RW i represents a river width, and LH i represents an overall length of the river.
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