Remote sensing water line distance water depth inversion method

Abstract

The invention provides a remote sensing water line distance water depth inversion method, and belongs to the field of remote sensing water depth inversion. Aiming at the problems that the distance between a water depth point with geographic coordinates and a water boundary line is in direct proportion to the water depth and the chlorophyll content of water in a near-shore area is relatively high, the method comprises the following steps: selecting wave bands such as longitude and latitude, the distance between a pixel and the water boundary line, a coast wave band, a blue wave band, a green wave band and a red wave band; the improved normalized water body index (MNDWI), the red edge chlorophyll vegetation index (RECI) and the normalized difference moisture index (NDWI) serve as feature influence factors, a remote sensing waterline distance water depth inversion model is constructed, the water depth is inverted, and the remote sensing image water depth inversion precision can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing water depth inversion, and in particular to a remote sensing water depth inversion method based on the distance from the water boundary. Specifically, it relates to a remote sensing water depth inversion method that takes into account the distance from the water boundary to the water depth. Background Art

[0002] There are two problems with water depth in the nearshore area: (1) The farther the water depth point with geographic coordinates is from the water boundary line, the more obvious the gradual increase in water depth; (2) The chlorophyll content of the water body in the nearshore area is relatively high. However, the mainstream remote sensing water depth inversion method basically only uses factors such as the blue, green, red and near-infrared bands and their ratios to invert water depth, and does not consider the impact of these two problems, which reduces the accuracy of remote sensing image inversion water depth. For example, patent number ZL201110035432.2 is a water depth inversion method based on the water permeability band ratio factor, which uses blue band / red band, blue band / near-infrared band, green band / red band, green band / near-infrared band as image factors, constructs a neural network water depth inversion model, and uses satellite remote sensing images to invert water depth. Patent No. ZL202110391470.5 is a patent for remote sensing water depth inversion tidal height correction method. It uses the logarithm of the green band and the blue band as influencing factors to construct a remote sensing water depth inversion tidal height correction model, and uses Landsat8 Oil remote sensing image data to invert water depth. Patent No. ZL202111400522.7 is a patent for a shallow sea water depth inversion method based on Sentinel-2A images. It uses the blue band, green band and red band as influencing factors to construct a ratio water depth inversion model, a logarithmic model and a ratio logarithmic model, and uses satellite remote sensing images to invert water depth.

[0003] To this end, the present invention selects longitude and latitude, the distance from the pixel to the water's edge, coastal band, blue band, green band, red band, red edge band 1, red edge band 2, near infrared narrow band 1, near infrared band, near infrared narrow band 2, water vapor band, panchromatic band, cirrus band, shortwave infrared band 1, shortwave infrared band 2, improved normalized difference water index (MNDWI), red edge chlorophyll vegetation index (RECl) and normalized difference water index (NDWI) as characteristic influencing factors, constructs a remote sensing water edge distance and water depth inversion model, inverts the water depth, improves the accuracy of water depth inversion from remote sensing images, and obtains a high-precision remote sensing inversion water depth map. Summary of the invention

[0004] In view of the problem that the mainstream remote sensing water depth inversion method does not take into account that the farther the water depth point with geographic coordinates is from the water boundary line, the more obvious the water depth gradually increases, and the high chlorophyll content of the water body causes large water depth inversion errors, the present invention selects longitude, latitude, distance from pixel to water edge line, coast band, blue band, green band, red band, red edge band 1, red edge band 2, near infrared narrow band 1, near infrared band, near infrared narrow band 2, water vapor band, short wave infrared band 1, short wave infrared band 2, improved normalized water body index, red edge chlorophyll vegetation index, and normalized difference moisture index as characteristic influencing factors, constructs a remote sensing water edge line distance water depth inversion model, and uses multispectral satellite remote sensing images to invert higher precision water depth.

[0005] The remote sensing water edge distance and water depth inversion method includes the following steps:

[0006] S1: Select longitude Lon, latitude Lat, distance FL from pixel to water edge, coast band b1, blue band b2, green band b3, red band b4, red edge band 1 b5, red edge band 2 b6, near infrared narrow band 1 b7, near infrared band b8, near infrared narrow band 2 b8A, water vapor band b9, short wave infrared band 1 b11, short wave infrared band 2 b12, improved normalized water index MNDWI, red edge chlorophyll vegetation index RECL, normalized difference water index NDWI as the influencing factors of water depth inversion characteristics, and form the characteristic vector .

[0007] S2: Construct a water depth inversion model.

[0008] (1)

[0009] Where y is the water depth, is the feature vector, is the weight vector, is a nonlinear mapping, and b is a bias.

[0010] According to the principle of risk minimization, constraints can be established:

[0011] (2)

[0012] In the formula, e i is the error, γ is the regularization parameter, and st represents the constraint condition.

[0013] By introducing the Lagrange multiplier α, the above formula can be transformed into:

[0014] (3)

[0015] According to the necessary conditions for determining the extreme value, we can calculate ω, b, e respectively. i , αi Taking partial differentials, we can finally get:

[0016] (4)

[0017] In the formula, is the kernel function, .

[0018] Writing this in matrix form:

[0019] (5)

[0020] Finally, the water depth inversion model can be established:

[0021] (6)

[0022] S3: Dataset composed of existing feature vectors and water depth data Calculate model parameters and construct a water depth inversion model to invert water depth.

[0023] The benefit of the present invention lies in that the existing mainstream remote sensing water depth inversion method does not take into account that the farther the water depth point with geographic coordinates is from the water boundary line, the more obvious the water depth gradually increases, and the high chlorophyll content of the water body causes a large error in water depth inversion. The present invention selects longitude, latitude, distance from pixel to water edge line, coast band, blue band, green band, red band, red edge band 1, red edge band 2, near infrared narrow band 1, near infrared band, near infrared narrow band 2, water vapor band, short wave infrared band 1, short wave infrared band 2, improved normalized water body index, red edge chlorophyll vegetation index, normalized difference moisture index as characteristic influencing factors, constructs a remote sensing water edge line distance water depth inversion model, and uses multi-spectral satellite remote sensing images to invert higher precision water depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The experimental process

[0025] Figure 2 The study area and its water depth

[0026] Figure 3 The water depth inversion result of dual-band logarithmic ratio

[0027] Figure 4 The scatter plot of the dual-band logarithmic ratio inversion depth and the measured depth

[0028] Figure 5 It is the water depth inversion result of multi-band logarithmic model

[0029] Figure 6 This is a scatter plot of water depth inverted by multi-band logarithmic model and measured water depth.

[0030] Figure 7 It is the result of inverting water depth by multi-factor combination model

[0031] Figure 8 This is a scatter plot of water depth inverted by multi-factor combination model and measured water depth

[0032] Fig. 9 The water depth inversion result of the remote sensing water edge distance water depth inversion model

[0033] Fig.10 The scatter plot of the water depth inverted by the remote sensing water edge distance and the measured water depth DETAILED DESCRIPTION

[0034] The experiments and steps of the present invention are further described in detail below with reference to the accompanying drawings.

[0035] Combination Figure 1 , describing the experimental process of this embodiment:

[0036] S1: This embodiment uses 3 Sentinel-2 images and 1 Landsat 9 image, and performs image preprocessing such as radiometric calibration, atmospheric correction, and reflectivity extraction on the images. Sentinel-2A images can be downloaded for free from the official website of the European Space Agency (ERS) (https: / / scihub.copernicus.eu / dhus / # / home) and the official website of the United States Geological Survey (USGS) (https: / / earthexplorer.usgs.gov / ). Landsat 9 images can also be downloaded from the USGS official website. In this experiment, a total of 3 Sentinel-2A satellite L1C-level images were downloaded, corresponding to October 24, 2021, November 26, 2021, and December 6, 2021. One Landsat 9 L2-level image was downloaded, corresponding to February 24, 2022. The detailed information of each band of Sentinel-2A and Landsat9 images is shown in Table 1.

[0037] Table 1 Sentinel-2 A and Landsat 9 image parameters

[0038]

[0039] The preprocessing of Sentinel-2 images can be divided into two steps: ① Atmospheric correction and radiometric calibration. The most mainstream method is to use the Sen2Cor software officially provided by the European Space Agency for processing. This article uses Sen2Cor version 2.10 to perform radiometric calibration and atmospheric correction on Sentinel-2 A images. It can be downloaded from the official website of the European Space Agency: https: / / step.esa.int / main / snap-supported-plugins / sen2cor / sen2cor-v2-10 / . ② Resampling. Since the bands of Sentinel-2 multispectral images have different resolutions, and their original formats are different from those opened directly with ENVI, resampling processing is required. This article also uses the Sentinel series satellite image processing software SNAP officially provided by the European Space Agency to resample the images. The download address is as follows: https: / / step.esa.int / main / download / snap-download / .

[0040] This article directly downloads Landsat 9 L2 products. L2 products have been officially calibrated for radiometric calibration and atmospheric correction. Therefore, no additional preprocessing is required. In ENVI, you can directly open the TIF format data of each band in the downloaded image data file.

[0041] S2: This embodiment uses the water depth data measured by multi-beam, and obtains the water depth data at the time of satellite transit after tidal correction, as training data and verification data.

[0042] Due to the influence of tides, the water depth near the coast varies in different time periods. The measured water depth data is generally based on the geoid to calculate the distance between the mean sea level and the seabed. Therefore, in remote sensing water depth inversion, it is necessary to perform tidal correction on the actual water depth of the experimental area at the time of remote sensing imaging. The calculation formula is as follows:

[0043] H=h+T refer +T (2-1)

[0044] Where H represents the actual water depth at the time of image acquisition; h represents the measured water depth below the geoid; T referIndicates the distance between the tidal reference surface and the geoid, which is usually a negative number; T indicates the tidal height below the tidal height reference surface at the time of image acquisition. The tidal values ​​in the Weizhou Island area can be queried on the website of the National Marine Information Center (https: / / www.cnss.com.cn / tide / ), and the tidal values ​​in the Hawaiian Islands area can be queried at the U.S. Meteorological Bureau (https: / / tides.willyweather.com / hi / honolulu / honolulu.html). The tidal values ​​of the three Sentinel-2 images in the corresponding period were 299cm, 404cm and 251cm respectively. The tidal height of Landsat 9 in the corresponding period was 10cm.

[0045] S3: Based on the blue-green dual-band, blue-green-yellow-red near-infrared multi-band, blue-green-yellow-red band multi-factor combination of satellite images of S1, as well as longitude, latitude, distance from pixel to water edge, coastal band, blue band, green band, red band, red edge band 1, red edge band 2, near-infrared narrow band 1, near-infrared band, near-infrared narrow band 2, water vapor band, short-wave infrared band 1, short-wave infrared band 2, improved normalized water index, red edge chlorophyll vegetation index, normalized difference moisture index data and water depth data of S2 at the time of satellite transit, a remote sensing image water depth dataset of the study area was constructed.

[0046] S4: Combined with the entire process of solar radiation passing through the atmosphere, crossing the water surface, reaching the bottom of the water, and being transmitted to the satellite sensor, the remote sensing water depth inversion factors are analyzed. The dual-band ratio model, multi-band logarithmic model, multi-factor combination model and remote sensing water edge distance water depth inversion model corresponding to the S3 data (see formula (1)) are used respectively. The optimal parameters of the inversion model are obtained by combining the least squares method and the risk minimization principle (see formulas (2)-(5)) to invert the water depth respectively.

[0047] S5: The actual water depth and the inverted water depth are represented by a scatter plot, and the R of the inverted water depth and the actual water depth is analyzed. 2 .

[0048] S6: Select the water depth inverted by the optimal water depth inversion model (the remote sensing water edge distance water depth inversion model of the present invention), perform tidal height correction, and obtain a water depth map.

[0049] Combination Figure 2 Description The experimental study area is located in the sea west of Weizhou Island, Guangxi Zhuang Autonomous Region (21°00′ N - 21°05′ N, 109°00′ E - 109°10′ E), with a maximum water depth of 21.64m.

[0050] Combination Figure 3-10The results and errors of the four models, namely, the dual-band ratio model, the multi-band logarithmic model, the multi-factor combination model and the remote sensing water edge line distance water depth inversion model, can be obtained, as shown in Table 2. As can be seen from Table 2, the water depth range, R 2 , RMSE and MAE are all better than the dual-band ratio model, multi-band logarithmic model and multi-factor combination model. It can be seen that the remote sensing water edge distance and water depth inversion model of the present invention can effectively improve the water depth inversion accuracy.

[0051] Table 2 Combination and error comparison of four water depth inversion models

[0052]

[0053] In summary, the present invention can improve the accuracy of water depth inversion to a certain extent, making the inverted water depth closer to the real water depth, and also more practical and valuable.

[0054] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Remote sensing water edge distance and water depth inversion method, characterized by The steps include: S1: Select longitude Lon, latitude Lat, distance FL from pixel to water edge, coast band b1, blue band b2, green band b3, red band b4, red edge band 1 b5, red edge band 2 b6, near infrared narrow band 1 b7, near infrared band b8, near infrared narrow band 2 b8A, water vapor band b9, short wave infrared band 1 b11, short wave infrared band 2 b12, improved normalized water index MNDWI, red edge chlorophyll vegetation index RECL, normalized difference water index NDWI as the influencing factors of water depth inversion characteristics, and form the characteristic vector ; S2: Constructing water depth inversion model: (1) Where y is the water depth, is the feature vector, is the weight vector, is a nonlinear mapping, and b is a bias; According to the principle of risk minimization, constraints can be established: (2) In the formula, e i is the error, γ is the regularization parameter, and st represents the constraint condition; By introducing the Lagrange multiplier α, the above formula can be transformed into: (3) According to the necessary conditions for determining the extreme value, we can calculate ω, b, e respectively. i , α i Taking partial differentials, we can finally get: (4) In the formula, is the kernel function, ; Writing this in matrix form: (5) Finally, the water depth inversion model can be established: (6) S3: Dataset composed of existing feature vectors and water depth data Calculate model parameters and construct a water depth inversion model to invert water depth.

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