CN111561916B - Shallow sea water depth uncontrolled extraction method based on four-waveband multispectral remote sensing image - Google Patents
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Abstract
The invention provides a shallow sea depth uncontrolled extraction method based on a four-waveband multispectral remote sensing image, which is characterized in that the existing logarithm ratio model and a semi-analysis model are fused according to the shallow sea depth measurement requirement of an ocean island reef, the macroscopic constraint is carried out on the regional water body optical parameters by utilizing the strong linear relation between the blue-green waveband reflectivity logarithm ratio of the logarithm ratio model and the depth of water, the shallow sea depth extraction based on the four-waveband multispectral remote sensing image without the support of actually measured data is realized, the application efficiency of the four-waveband multispectral remote sensing image with the longest historical accumulation time, the richest remote sensing platform and the most extensive application is greatly improved, the method is an innovation in the aspect of remote sensing information technology application, is a beneficial supplement for island reef shallow sea depth measurement, and has great practical value.
Description
Technical Field
The invention belongs to the field of remote sensing technology application and seabed topography mapping, and particularly relates to a shallow sea water depth extraction method under actual measurement-free water depth control (uncontrolled for short) based on a four-waveband multispectral remote sensing image.
Background
Submarine topography mapping is the first step in exploring and studying the oceans, is the primary condition for developing and protecting oceans, and is an urgent need for oceanographic research nowadays. Before the invention of an echo depth finder, the depth of water is measured mainly by a depth measuring rod and a depth measuring hammer, and the measurement precision is poor. The drawing of the chart in the sea in the modern sense is realized after the appearance of the echo sounder in the 20 th century. However, the early depth sounder is a single-beam transmitter, and only the water depth right below the measuring ship can be obtained by one-time transmission, so that only point and line measurement can be realized, and the landform and the geomorphology between the measuring lines cannot be reflected. The multi-beam sounding technology appeared in the middle of 1970 s realizes the strip-shaped measurement (the maximum width can reach 7 times of the depth of water), and the sounding efficiency is obviously improved. Nevertheless, in the task of measuring the wide-range shallow sea terrain, the disadvantages of long measuring period, large manpower consumption and high capital requirement still exist. Especially, the multi-beam measurement in the shallow sea area (the water depth is less than 20m) of the island is limited by the navigation safety and the measurement width of the ship platform, and the island water depth measurement work is severely restricted.
With the appearance and development of remote sensing technology, a series of optical remote sensing depth measurement methods are researched and provided according to the relation between the marine water body scattering and seabed reflection characteristic information contained in an optical remote sensing image and the water depth, and the methods mainly comprise a semi-theoretical semi-empirical model and a semi-analytical model. The semi-theoretical semi-empirical model mainly comprises a single-waveband model, a double-waveband or multi-waveband polynomial model and a double-waveband logarithmic ratio model, and has certain application in different types of shallow sea water depth measurement. However, the method is based on water body spectral information to detect water depth, so the applicability of the method is influenced by the water body environment, and because the existing optical radiation transmission model is difficult to accurately describe the complex water body scattering characteristics, the method usually needs to actually measure the water body optical parameters or the water depth data to support training to effectively work, thereby limiting the application of the methods. The semi-analytical model is a water depth extraction method developed based on hyperspectral remote sensing data, and although the uncontrolled extraction of the ocean shallow sea water depth is realized, the semi-analytical model is limited to the low acquisition capacity and data accumulation of the hyperspectral remote sensing data and cannot be popularized and applied. The four-waveband multispectral remote sensing image has the longest historical accumulation time, the most abundant remote sensing platform and the most extensive application. Therefore, development of shallow sea water depth uncontrolled extraction based on a four-waveband multispectral remote sensing image without support of measured data is urgently needed.
The method aims at the requirements of island reef shallow sea water depth measurement, and extracts shallow sea water depth information without the support of actually measured data by using a four-waveband multi-spectrum optical remote sensing image.
Disclosure of Invention
The invention aims to provide a novel shallow sea water depth uncontrolled extraction method based on a four-waveband multispectral remote sensing image.
The invention is realized by the following technical scheme:
a shallow sea water depth uncontrolled extraction method based on a four-waveband multispectral remote sensing image comprises the following steps:
(1) preprocessing the four-waveband multispectral remote sensing image to obtain a four-waveband multispectral reflectivity image; the pretreatment refers to atmospheric correction, radiation correction and the like, and can be realized by adopting the existing software.
(2) Determining a shallow sea water depth extraction working area;
(3) randomly generating self-learning points;
(4) calculating the logarithmic ratio of the blue-green band reflectivity of the self-learning point;
(5) calculating the water depth of the self-learning point;
(6) determining parameters of a shallow sea water depth calculation model in a working area to obtain a water depth calculation model;
(7) and calculating the water depth of all pixels in the working area according to the water depth calculation model.
In the above technical solution, further, the method for determining the working area in step (2) includes: by setting the threshold values of the blue light band reflectivity (Rb) and the green light band reflectivity (Rg), the pixel points of which both Rb and Rg are in a specific range (e.g., between 0.005 and 0.1) are extracted as the working area.
Further, in step (3), n random points, where 50 ≦ n ≦ 200, are generated as self-learning points SSP [ i ] (i ═ 1,2,3, …, n) within the working area determined in step (2).
Further, in step (4), according to the spatial position of the self-learning point SSP [ i ] determined in step (3), extracting the blue band reflectivity Rb [ i ] and the green band reflectivity Rg [ i ] of the corresponding positions, and calculating the logarithmic ratio value Ci of the blue band reflectivity and the green band reflectivity:
where K is a constant that ensures that the logarithm is positive, and is typically 10000.
Further, in the step (5), the water depth value D [ i ] is estimated for the self-learning point SSP [ i ] generated in the step (3), and the specific estimation step is as follows:
1) setting initial values, search ranges and step lengths of the optical parameters of the water body. According to the parameter setting of the semi-analytical model, three optical parameters of the water body are respectively the absorption coefficient P of phytoplankton at 440nm, the absorption coefficient G of yellow substances and debris at 440nm, the observation angle of the comprehensive sensor and the influence X of sea conditions on the backscattering coefficient of the suspended particles; by setting initial values, search ranges, step lengths and numbers of P, G, X three parameters, for example, p, g and x parameters are respectively taken, M groups of P, G, X parameter combinations are obtained, where M is the product M of P, G, X parameter values, which is pgx, for example: the following table example is taken for each parameter, i.e. M20 × 6 × 15 1800 can be calculated according to the following table:
the smaller the step size, the larger the number, the higher the accuracy, but the larger the amount of calculation, so the setting can be made as the case may be.
2) The four-band reflectivity of the self-learning point SSP [ i ] is read, M groups of different P, G, X combinations are sequentially input, and the water depth value H of the self-learning point SSP [ i ] is estimated by using a semi-analytical model to obtain M groups of H [ i ].
3) From M groups H [ i ] in turn]Extracting a group of H [ i]With the C [ i ] obtained in step (4)]Together, H ═ m according to the log ratio model1*C+m0And performing linear fitting by using a least square method to obtain m1And m0And obtaining H [ i ]]And Cj]Is related to coefficient R2. Selecting the correlation coefficient R2Highest groupH[i]As a self-learning point SSP [ i ]]Corresponding optimal estimated water depth value D [ i ]]。
Further, the self-learning point SSP [ i ] obtained in step (6) according to step (5)]Corresponding optimal estimated water depth value D [ i ]]And m corresponding thereto1And m0And according to the logarithmic ratio model, establishing a calculation model of the estimated water depth value h of each pixel of the whole working area:
where j denotes the picture element j of the working area.
Further, in the step (7), according to the working area water depth estimation model determined in the step (6), water depth values of all pixels are calculated, and shallow sea water depth extraction work is completed.
In particular, optical remote sensing is a method for non-contact sensing by using reflection and scattering signals of sunlight. For the remote sensing detection of shallow sea water depth, sunlight reaches the seabed through the processes of absorption and scattering of atmosphere, reflection and refraction of a sea-air interface, absorption and scattering of a water body and the like, and is received by a sensor after being reflected by the seabed through the absorption and scattering of the water body, the reflection and refraction of the sea-air interface and the absorption and scattering of the atmosphere, so that a multiband optical remote sensing image is obtained. Therefore, the radiation transmission process of ocean optical remote sensing is complex, only partial signals which reach the seabed and are transmitted back to the sensor carry water depth information, and the method can be used for shallow sea water depth detection.
Multi-beam and single-beam sounding has become the main means of current shallow sea depth measurement, but limited by measurement period, manpower consumption and capital, there are many areas which cannot be measured in full coverage, and updating measurement is more insufficient, and in addition, effective measurement is more difficult for the extremely shallow area with 20m near the bank of ocean island reefs. Such as: the log ratio model can utilize a significant linear relationship between the log ratio of the reflectivity of the blue-green wave band and the water depth, and can realize the detection of the water depth under the support of the measured water depth data (Stumpf R.P., K.Holderid, and M.Sinclair, "Determination of water depth with high-resolution imaging variable bottom types," Limnol. Oceanogr, vol.48, 547-556, Jan.2003), but it is difficult to obtain a large amount of measured water depth for the shallow reef sea area of the oceanic island. Meanwhile, a semi-analysis model supported by actually measured water depth data is not needed, shallow sea water depth calculation based on Hyperspectral remote sensing data can be achieved (Lee Z., K.L.truck, C.D.Mobley, R.G.Steward, and J.S.Patch, "Hyperspectral remote sensing for shallow waters.I.A. analytical model," app.Opt., vol.37, No.27, pp.6329-6338,1998.), but the method is difficult to be applied to shallow sea water depth detection of a four-band multispectral remote sensing image.
Aiming at the ocean island reef shallow sea water depth measurement requirement, based on the reasonable assumption that the difference of the water body optical parameters of the local ocean shallow sea water area is small, the defect that a semi-analytical model cannot be directly used for water depth extraction of a four-waveband multispectral remote sensing image is overcome by setting the combination of the water body environment parameters P, G, X, then the regional water body optical parameters are subjected to macroscopic constraint by utilizing the strong linear relation between the blue-green waveband reflectivity logarithmic ratio of the logarithmic ratio model and the water depth, and a group of optimal P, G, X parameter combinations are determined, so that a water depth calculation model suitable for the whole working area is established, and the problem of water depth extraction of the four-waveband multispectral remote sensing under the condition of no-field actual measurement water depth control is well solved. The method greatly improves the application efficiency of the four-waveband multispectral remote sensing image with the longest historical accumulation time, the most abundant remote sensing platform and the most extensive application, is an innovation in the application of remote sensing information technology, is a beneficial supplement to island shallow sea water depth measurement, and has great practical value.
Drawings
FIG. 1 is a schematic flow chart of a shallow sea water depth uncontrolled extraction method based on a four-waveband multispectral remote sensing image.
Fig. 2 is a schematic diagram of a workspace.
Fig. 3a and 3b show the correlation result of the water depths of the logarithmic ratio model and the semi-analysis model under the combination (P, G, X) of two typical water body optical parameters. Wherein FIG. 3a has a lower correlation, indicating that the water optical parameter combination (P, G, X) does not reflect the actual water optical characteristics; FIG. 3b shows a higher correlation, indicating that the combination of water optical parameters (P, G, X) reflects the actual water optical characteristics.
FIG. 4 is a comparison graph of water depth data extracted based on a GeoEye-1 satellite four-waveband multispectral remote sensing image and laser radar depth measurement data of a corresponding area.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
According to the experiment of the shallow sea water depth uncontrolled extraction method based on the four-waveband multispectral remote sensing image, the technical route is shown in figure 1, and the experiment specifically comprises the following steps:
(1) preprocessing the four-waveband multispectral remote sensing image to obtain a four-waveband multispectral reflectivity image; if the radiation correction and atmospheric correction functions of the ENVI5.0 (or higher version) remote sensing image processing software can be adopted, the radiation correction and atmospheric correction processing can be carried out on the four-waveband multispectral remote sensing image, and therefore the four-waveband multispectral reflectivity image can be obtained.
(2) Determining a shallow sea water depth extraction working area; by setting the threshold values of the blue-band reflectivity Rb and the green-band reflectivity Rg, the pixel points with Rb and Rg both in a specific range (e.g., 0.005, 0.1) are extracted as the working area, such as the shallow sea area in fig. 2.
(3) Randomly generating self-learning points; and generating n random points, wherein n is more than or equal to 50 and less than or equal to 200, as self-learning points SSP [ i ] (i is 1,2,3, …, n).
(4) Calculating the logarithmic ratio of the blue-green band reflectivity of the self-learning point; determining the space position of self-learning point SSP [ i ], extracting the blue light band reflectivity Rb [ i ] and the green light band reflectivity Rg [ i ] of the corresponding positions, and calculating the logarithmic ratio value Ci of the blue and green band reflectivities:
where K is a constant that ensures that the logarithm is positive, and is typically 10000.
(5) Calculating the water depth of the self-learning point;
and for the self-learning point SSP [ i ], estimating a water depth value D [ i ], wherein the estimation steps are as follows:
1) setting initial value, search range and step length of optical parameters of water body
Setting the initial value of the absorption coefficient P of phytoplankton at 440nm to be 0.005, the search range to be 0.005-0.1 and the step length to be 0.005 according to the parameter setting of the semi-analytical model; setting the initial value of the absorption coefficient G of the yellow substance and the crumbs at 440nm to be 0.005, the search range to be 0.005-0.03 and the step length to be 0.005; setting the initial value of the influence X of the observation angle and the sea state of the comprehensive sensor on the backscattering coefficient of the suspended particles to be 0.01, the search range to be 0.01-0.15 and the step length to be 0.01; setting the number of required parameters to obtain M groups of P, G, X parameter combinations, wherein M is the product of the values of P, G, X three parameters;
2) reading the four-band reflectivity of the self-learning point SSP [ i ], sequentially inputting M groups of different P, G, X parameter combinations, and estimating the water depth value H of the self-learning point SSP [ i ] by using a semi-analysis model to obtain M groups of H [ i ];
according to the literature (Lee z., k.l. truck, c.d. mobley, r.g. steward, and j.s.patch, "Hyperspectral remote sensing for show water devices.i.a. semi analytical model," appl.opt., vol.37, No.27, pp.6329-6338,1998.), the semi-analytical model requires Hyperspectral remote sensing images to extract water depth data, but cannot directly extract water depth data based on four-waveband multispectral images using the semi-analytical model. The present invention can calculate the water depth value by using a semi-analytical model given P, G, X combination.
3) From M groups H [ i ] in turn]Extracting a group of H [ i]With the C [ i ] obtained in step (4)]Together, H ═ m according to the log ratio model1*C+m0And performing linear fitting by using a least square method to obtain m1And m0And obtaining H [ i ]]And Cj]Is related to coefficient R2(ii) a Selecting the correlation coefficient R2Highest group of H [ i ]]As a self-learning point SSP [ i ]]Corresponding optimal estimated water depth value D [ i ]](ii) a As shown in FIG. 3, according to two groups of H [ i ]]Relation between water depth value extracted by determined logarithmic ratio model parameters and water depth of semi-analysis modelFIG. 3a shows the result with a smaller correlation coefficient than the result of FIG. 3b, when the set of H [ i ] with the highest correlation coefficient is obtained]Then, the optimal estimated water depth D [ i ] for the semi-analytical model is determined]And obtaining its corresponding m1And m0。
(6) Determining parameters of a shallow sea water depth calculation model in a working area to obtain a water depth calculation model;
using self-learning point SSP [ i ]]Corresponding optimal estimated water depth value D [ i ]]And m corresponding thereto1And m0And according to the logarithmic ratio model, establishing a calculation model of the estimated water depth value h of each pixel of the whole working area:
where j denotes the picture element j of the working area.
(7) And calculating the water depth of all pixels in the working area according to the water depth calculation model.
And inputting the reflectivity of the blue-green wave band pixel by using the established water depth calculation model, and calculating the water depth value to obtain the water depth data of the working area. FIG. 4 is a comparison graph of water depth data extracted based on a GeoEye-1 satellite four-waveband multispectral remote sensing image and laser radar depth measurement data of a corresponding area. As can be seen from FIG. 4, the water depth data obtained by the water depth calculation model determined by the method of the present invention is substantially consistent with the measured data. The invention firstly overcomes the defect that a semi-analysis model can not be directly used for water depth extraction of the four-waveband multispectral remote sensing image by setting the combination of water environment parameters P, G, X, and then determines a group of optimal P, G, X combinations by utilizing the characteristic that the water depth data Hi determined by a logarithm ratio model is linearly related to the logarithm ratio Ci of a blue-green waveband, thereby determining a water depth calculation model suitable for the whole working area and well solving the problem of water depth extraction of the four-waveband multispectral remote sensing image under the condition of no field actual measurement water depth control.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are included in the scope of the present invention.
Claims (5)
1. A shallow sea water depth uncontrolled extraction method based on a four-waveband multispectral remote sensing image is characterized by comprising the following steps:
(1) preprocessing the four-waveband multispectral remote sensing image to obtain a four-waveband multispectral reflectivity image;
(2) determining a shallow sea water depth extraction working area;
(3) randomly generating self-learning points;
(4) calculating the logarithmic ratio of the blue-green band reflectivity of the self-learning point;
(5) calculating the water depth of the self-learning point;
(6) determining parameters of a shallow sea water depth calculation model in a working area to obtain a water depth calculation model;
(7) calculating the water depth of all pixels in the working area according to the water depth calculation model;
wherein, in the step (5), the self-learning point SSP [ i ] generated in the step (3) is estimated to obtain a water depth value D [ i ], and the specific estimation step is as follows:
1) setting initial value, search range and step length of optical parameters of water body
According to the parameter setting of the semi-analytical model, setting an absorption coefficient P of phytoplankton at 440nm, an absorption coefficient G of yellow substances and debris at 440nm, an initial value, a search range, a step length and the required number of the coefficient influence X of the observation angle of the comprehensive sensor and sea conditions on the backscattering of suspended particles, and obtaining M groups of P, G, X parameter combinations, wherein M is the product of the required number of each parameter;
2) reading the four-band reflectivity of the self-learning point SSP [ i ], sequentially inputting M groups of different P, G, X parameter combinations, and estimating the water depth value H of the self-learning point SSP [ i ] by using a semi-analysis model to obtain M groups of H [ i ];
3) from M groups H [ i ] in turn]Sequentially extracting H [ i ]]With the C [ i ] obtained in step (4)]H-m according to the log ratio model1*C+m0And performing linear fitting by using a least square method to obtain m1And m0And obtaining H [ i ]]And C [ i ]]Is related to coefficient R2(ii) a Selecting the correlation coefficient R2Highest group of H [ i ]]As a self-learning point SSP [ i ]]Corresponding optimal estimated water depth value D [ i ]]。
2. The shallow sea water depth uncontrolled extraction method based on the four-waveband multispectral remote sensing image as recited in claim 1, wherein the method for determining the working area in the step (2) comprises the following steps: and extracting pixel points with Rb and Rg both in a specific range as a working area by setting threshold values of the reflectivity Rb of the blue light wave band and the reflectivity Rg of the green light wave band.
3. The shallow sea water depth uncontrolled extraction method based on the four-waveband multispectral remote sensing image as claimed in claim 1, characterized in that in the step (3), n random points are generated within the working area determined in the step (2), wherein n is greater than or equal to 50 and less than or equal to 200, and the random points are used as self-learning points SSP [ i ] (i is 1,2,3, …, n).
4. The method for the uncontrolled extraction of the shallow sea water depth based on the four-band multispectral remote sensing image as claimed in claim 1, wherein in the step (4), according to the spatial position of the self-learning point SSP [ i ] determined in the step (3), the blue-band reflectivity Rb [ i ] and the green-band reflectivity Rg [ i ] of the corresponding positions are extracted, and the logarithmic ratio Ci of the blue-band reflectivity and the green-band reflectivity is calculated:
where K is a constant that ensures that the logarithm is positive, and is taken to be 10000.
5. The method for the uncontrolled extraction of the shallow sea depth of water based on the four-band multispectral remote sensing image as claimed in claim 1, wherein the self-learning point SSP [ i ] obtained in step (6) according to step (5)]Corresponding optimal estimated water depth value D [ i ]]And m corresponding thereto1And m0And according to the logarithmic ratio model, establishing a calculation model of the estimated water depth value h of each pixel of the whole working area:
where j denotes the picture element j of the working area.
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