Uncontrolled multispectral remote sensing water depth inversion method and system

Abstract

The invention discloses an uncontrolled multispectral remote sensing water depth inversion method and system, and belongs to the field of shallow sea water depth inversion methods. The method comprises the following steps: firstly, by calculating a depth invariant index of a target area, dividing the target area into a high substrate reflectivity type, a medium substrate reflectivity type and a low substrate reflectivity type; establishing corresponding high, medium and low substrate reflection spectrum sets according to the typical substrate type of the target area; and finally, based on a shallow water radiation transmission model, combining remote sensing reflectivity data of an adjacent optical deep water region, and establishing optimal matching between the simulated remote sensing reflectivity and the actual remote sensing reflectivity by matching a substrate reflection spectrum in a regional pixel-by-pixel manner, so as to obtain a continuous large-area bathymetric map of the target region. The method is suitable for water depth measurement without prior actual measurement of water depth in a multispectral remote sensing scene, solves the problem that a traditional water depth inversion algorithm does not fully consider complex substrate types and spectral variation, and provides a higher-precision method for shallow sea multispectral remote sensing uncontrolled water depth inversion.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology application and water depth inversion method, and in particular to an uncontrolled multi-spectral remote sensing water depth inversion method and system based on pixel-by-pixel matching of bottom spectra. Background Art

[0002] The shallow marine environment is one of the ecological environments that is inseparable from humans, and has important economic and ecological values. For shallow sea areas, water depth information is a relatively important shallow sea information element. Since water depth is closely related to ocean temperature, phytoplankton concentration, chlorophyll concentration, seabed type, etc., water depth information is related to the advancement of shallow sea aquaculture, maritime transportation, marine engineering construction, island coastal zone management and other projects.

[0003] With the development of passive remote sensing, the information of atmosphere, water body and bottom can be obtained through the radiation data of passive remote sensing by satellite platforms. Many remote sensing data have been applied to the estimation of shallow water depth information. Compared with traditional shipborne or airborne sounding, satellite remote sensing sounding has quickly become the focus of shallow water sounding due to its advantages of low measurement cost, high spatial coverage and short measurement cycle. From the perspective of optical remote sensing water depth inversion algorithm, it is mainly divided into empirical algorithms based on statistical models and semi-analytical algorithms based on the radiation transmission mechanism of light. In addition, the application scenarios of these two types of algorithms are also different. The former is mostly used in shallow water areas with prior water depth data, and the latter is mostly used in shallow water areas without prior water depth data.

[0004] However, areas with complex bottom types will produce bottom spectral variations, which will bring errors to the uncontrolled multi-spectral remote sensing depth inversion algorithm that does not require prior depth data, affecting the accuracy of depth inversion. Summary of the invention

[0005] In view of this, the purpose of the present invention is to solve the error problem of the water depth inversion algorithm caused by the variation of the bottom reflectance spectrum in areas with complex seabed sediments, and to provide an uncontrolled multi-spectral remote sensing water depth inversion method based on pixel-by-pixel matching of the bottom sediment spectrum to improve the water depth inversion accuracy.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] The present invention discloses an uncontrolled multi-spectral remote sensing water depth inversion method, the method comprising:

[0008] Preprocess multispectral satellite remote sensing data;

[0009] For the pre-processed multispectral satellite remote sensing data, the grayscale map of the bottom reflectance is obtained based on the depth invariance index, and the target area is divided into three types of bottom reflectance areas: high, medium and low.

[0010] Construct a collection of bottom spectra for each type of bottom reflectance area;

[0011] The optimized water depth is obtained by using a multispectral remote sensing uncontrolled water depth inversion algorithm. In the multispectral remote sensing uncontrolled water depth inversion algorithm, the remote sensing reflectivity of the optical deep water near the target research area is used to obtain the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G, and the nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and the water depth H is obtained; the bottom reflectance spectrum is matched pixel by pixel in each region, and the Levenberg-Marquardt optimization algorithm is used to establish the best match between the simulated and actual remote sensing reflectivity, and finally the water depth inversion result of the target area is obtained.

[0012] Furthermore, the method further comprises:

[0013] Preprocess the measured water depth data;

[0014] The water depth result obtained by the uncontrolled multispectral remote sensing water depth inversion algorithm is tested based on the preprocessed measured water depth data to verify the accuracy of the uncontrolled multispectral remote sensing water depth inversion algorithm.

[0015] Furthermore, the method further comprises:

[0016] Create a continuous water depth map, input the remote sensing reflectance of the target area's optical shallow water area, obtain the optimal water depth corresponding to each pixel, and output a continuous water depth diagram.

[0017] Furthermore, the preprocessing of multispectral satellite remote sensing data includes: atmospheric correction, glare correction and water-land separation.

[0018] Furthermore, the preprocessing of the measured water depth data includes: alignment of the spatial resolution of the measured water depth data with the spatial resolution of the remote sensing image, and tidal correction.

[0019] Furthermore, a grayscale map of the bottom reflectance is obtained based on the depth invariance index, and the target area is divided into three types of bottom reflectance areas: high, medium, and low, including:

[0020] Select the blue band and the green band to calculate the depth invariance index Y and obtain the corresponding grayscale image. The calculation formula of the depth invariance index Y is:

[0021]

[0022] Among them, i and j are blue band and green band; L i is the radiance value of band i; L si is the optical deep water i-band radiance value that is not affected by the water surface and atmosphere; K i and K j is the diffusion attenuation coefficient of the water body in band i and band j, which is calculated by the following formula:

[0023]

[0024] Among them, X i =ln(L i -L si ), L i L is the i-band optical shallow water radiance in the area with varying depth but uniform bottom quality; si is the optical deep-water radiance in band i. Similarly, X j is the corresponding value of band j; σ ii Yes X i The variance, σ jj Yes X j The variance, σ ij Yes X i and X j The covariance of

[0025] The optimal threshold is obtained by using Otsu's threshold segmentation method. Two optimal thresholds are obtained by using Otsu's threshold segmentation method on the upper and lower parts of the threshold respectively. Based on these two optimal thresholds, three-partition images with high, medium and low reflectivity are obtained.

[0026] Furthermore, a set of bottom spectra for each type of bottom reflectivity area is constructed, including:

[0027] The representative bottom spectra in the study area are divided into two categories: high and low reflectance. The mean of the bottom spectra of the high reflectance category is used as the initial spectrum of the high reflectance area, the mean of the bottom spectra of the low reflectance category is used as the initial spectrum of the low reflectance area, and the mean of all the spectra representing the bottom is used as the initial spectrum of the medium reflectance area. The bottom spectrum set of each category includes: the typical bottom type reflectance spectrum in the current partition; for the pixels with bottom spectrum variation in the current partition, a mixed spectrum is established:

[0028]

[0029] Where λ is the wavelength; ρ' sd (λ) is the set of bottom sediment spectra affected by the bottom sediments of adjacent partitions; is the initial substrate spectrum of the current category; is the initial bottom spectra of adjacent categories; s1 and s2 are proportional coefficients, the sum of the two is 1 and s1 is not less than 0.5, and the step size is 0.2; the obtained high, medium and low bottom reflectance sets are respectively expressed as and

[0030] The bottom reflectance is modeled using the above method and expressed as:

[0031]

[0032] Where, ρ(λ) is the substrate reflectivity; B is the substrate reflectivity at 550nm.

[0033] Furthermore, the nonlinear relationship between the subsurface remote sensing reflectivity near the optical deep water area and the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G is:

[0034]

[0035]

[0036] a(λ)=a w (λ)+[a0(λ)+a1(λ)ln(P)]P+Ge -S(λ-440) ;

[0037]

[0038] P = 0.06C 0.65 ;

[0039] X=0.0111C 0.62 ;

[0040] in, is the optical deep water subsurface remote sensing reflectivity; u(λ) is the intrinsic optical parameter of the water body; g0 and g1 are empirical parameters. For Class 1 water bodies, g0≈0.0949, g1≈0.0794; a(λ) and b b (λ) are the total absorption coefficient and backscattering coefficient of water body respectively; a w (λ) and b bw (λ) are the pure water absorption coefficient and pure water backscattering coefficient respectively; a0(λ) and a1(λ) are empirical coefficients; P is the phytoplankton absorption coefficient at 550nm; G is the yellow matter absorption coefficient at 550nm; X is the suspended particle absorption coefficient at 550nm; C is the chlorophyll a concentration at 550nm.

[0041] Furthermore, the nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and the water depth H is:

[0042]

[0043]

[0044]

[0045]

[0046] K(λ)=a(λ)+b b (λ);

[0047] Among them, R rsm (λ) is the modeled remote sensing reflectance; r rs m (λ) is the remote sensing reflectivity of the modeled water subsurface; θ w is the subsurface solar zenith angle; K(λ) is the diffusion attenuation coefficient; is the optical deep-water subsurface remote sensing reflectivity; and is the optical path extension coefficient for water and bottom scattering.

[0048] In another aspect, the present invention further provides an uncontrolled multi-spectral remote sensing water depth inversion system, the system comprising:

[0049] The preprocessing module is used to perform atmospheric correction, glare correction and water-land separation on the input remote sensing image data; it is also used to perform tidal correction and data registration on the measured water depth;

[0050] The bottom processing module is used to calculate the depth invariance index of the pre-processed multispectral satellite remote sensing data, obtain a grayscale image representing the size of the bottom reflectance, and divide it into three categories: high, medium and low according to the level of bottom reflectance, and construct a set of bottom reflectance spectra for each category;

[0051] The inversion water depth module is used to obtain the optimized water depth using a multispectral remote sensing uncontrolled water depth inversion algorithm. In the multispectral remote sensing uncontrolled water depth inversion algorithm, the remote sensing reflectivity of the optical deep water near the target research area is used to obtain the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G, and the nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and the water depth H is obtained; the bottom reflectance spectrum is matched pixel by pixel in each region, and the Levenberg-Marquardt optimization algorithm is used to establish the best match between the simulated and actual remote sensing reflectivity, and finally the water depth inversion result of the target area is obtained.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] The uncontrolled multi-spectral remote sensing water depth inversion method of the present invention first performs bottom sediment partitioning on the study area by using the depth invariant index, and secondly considers the problem of bottom sediment spectrum variation, and constructs a bottom sediment spectrum set including mixed bottom sediment on the basis of the bottom sediment partitioning, fully considers the possible bottom sediment reflectance spectrum of the study area, reduces the inversion error caused by the problem of bottom sediment reflectance spectrum variation, and can obtain better water depth inversion accuracy by using two visible light bands without the need for measured water depth data, thereby effectively improving the accuracy of the uncontrolled multi-spectral remote sensing water depth inversion algorithm.

[0054] The present invention can be applied to water depth measurement without the need for a priori measured water depth in multispectral remote sensing scenarios, solving the problem that traditional water depth inversion algorithms do not fully consider the complexity of bottom types and spectral variation, and provides a higher-precision method for shallow-water multispectral remote sensing uncontrolled water depth inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 is a flow chart of an uncontrolled multi-spectral remote sensing water depth inversion method according to an embodiment of the present invention;

[0057] Figure 2 1 is an example diagram of substrate grayscale size and substrate partition in an embodiment of the present invention;

[0058] Figure 3 It is the Sentinel-2 original image map and the shallow sea water depth inversion result map in the embodiment of the present invention;

[0059] Figure 4 It is a scatter plot comparing the estimated water depth value and the measured water depth value in the embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0061] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] like Figure 1 As shown, an uncontrolled multi-spectral remote sensing water depth inversion method based on pixel-by-pixel matching of bottom spectra in an embodiment of the present invention includes the following steps:

[0063] Step 1: Preprocess multispectral satellite remote sensing data;

[0064] Among them, the multispectral satellite remote sensing data in this embodiment uses multispectral satellite remote sensing Sentinel-2L1C level data. The preprocessing of multispectral satellite remote sensing data includes at least: atmospheric correction, glare correction and water-land separation. Specifically, atmospheric correction can be performed using Sen2Cor, the official processing tool of Sentinel-2; water-land separation and glare correction can be completed by band calculation using SNAP software; band calculation specifically includes: using multiple glare pollution areas (in the specific implementation, four glare pollution areas are used) to perform linear regression between the near-infrared band radiance and the visible light band radiance, and then performing band calculation on the target band using the following formula:

[0065] L i (VIS)′=L i (VIS)-b i [L i (NIR)-L min (NIR)];

[0066] Among them, L i (VIS) is the radiance of visible light before glare correction, L i (VIS)' is the radiance of the visible light band after correction, L i (NIR) is the radiance in the near infrared band, L min (NIR) is the minimum radiance in the near infrared band, b i is the regression slope.

[0067] The normalized water mass index (NDWI) is used to separate land from water. The NDWI calculation formula is:

[0068]

[0069] Among them, L(Green) is the radiance value of the green band, and L(NIR) is the radiance value of the near-infrared band.

[0070] Step 2: preprocessing the measured water depth data;

[0071] Among them, the preprocessing of the measured water depth data at least includes: alignment of the spatial resolution of the measured water depth data with the spatial resolution of the remote sensing image, and tidal correction. Specifically, the alignment of the spatial resolution of the measured water depth data with the spatial resolution of the remote sensing image can use ArcGIS software to align the measured water depth points with the spatial resolution of the remote sensing image, so that the water depth point data and the remote sensing image pixels match one by one. Tidal correction can be combined with tide tables to calibrate the water depth data to be consistent with the actual water depth when the remote sensing image is acquired.

[0072] Specifically, the following formula is used for tidal correction:

[0073] H o =Z-tide z +tide image ;

[0074] Among them, H o is the actual water depth when the satellite passes; Z is the uncorrected measured water depth; tide z It is the tidal value when measuring water depth; tide image is the tidal value at the moment of satellite transit.

[0075] In ArcGIS software, the water depth values ​​of the measured water depth data points projected in the same remote sensing image pixel area are weighted averaged, and the calculation formula is:

[0076]

[0077] Where n is the number of data in the same pixel, Z is the data after registration; Z Ld It is the measured water depth data before alignment.

[0078] In this embodiment, the tide table is queried to obtain the tide height of 52 cm at the time of measured data collection, and the tide height of 115 cm at the time of remote sensing image data collection, and the tide correction formula is used for tide correction. In ArcGIS software, the water depth values ​​of the measured water depth data points projected in the same remote sensing image pixel area are weighted averaged, and finally 342 sets of measured water depth data after registration are obtained.

[0079] Step 3: Based on the preprocessed multispectral satellite remote sensing data, a grayscale map of the bottom reflectance is obtained based on the depth invariance index, and the target area is divided into three types of bottom reflectance areas: high, medium, and low;

[0080] Specifically, the blue band and the green band are selected to calculate the depth invariance index Y and obtain the corresponding grayscale image. The calculation formula of the depth invariance index Y is:

[0081]

[0082] Among them, i and j are blue band and green band; Li is the radiance value of band i; L si is the optical deep water i-band radiance value that is not affected by the water surface and atmosphere; K i and K j is the diffusion attenuation coefficient of the water body in band i and band j, which is calculated by the following formula:

[0083]

[0084] Among them, X i =ln(L i -L si ), L i L is the i-band optical shallow water radiance in the area with varying depth but uniform bottom quality; si is the optical deep-water radiance in band i. Similarly, X j is the corresponding value of band j; σ ii Yes X i The variance, σ jj Yes X j The variance, σ ij Yes X i and X j The covariance of .

[0085] Then, the Otsu threshold segmentation method is used to obtain the optimal threshold. Two optimal thresholds are obtained by using the Otsu threshold segmentation method on the upper and lower parts of the threshold. Based on these two optimal thresholds, three-division images of high, medium and low reflectance are obtained. Figure 2 shown.

[0086] Step 4, constructing a bottom sediment spectrum set for each type of bottom sediment reflectance area;

[0087] Specifically, the representative bottom spectra in the study area are divided into two categories: high and low reflectance. The average spectrum of typical bottom materials (sand, rock, gravel) of the high reflectance class is used as the initial spectrum of the high reflectance area, the average spectrum of the bottom materials (seaweed, coral, algae) of the low reflectance class is used as the initial spectrum of the low reflectance area, and the average spectrum of all representative bottom materials is used as the initial spectrum of the medium reflectance area. For the pixels of each partition, the bottom spectrum set of each category is established according to the following rules:

[0088] (1) Reflectance spectra of typical sediment types in the current subregion;

[0089] (2) For pixels with sediment spectrum variation in the current partition, a mixed spectrum is established:

[0090]

[0091] Where λ is the wavelength; ρ' sd(λ) is the set of bottom sediment spectra affected by the bottom sediments of adjacent partitions; is the initial substrate spectrum of the current category; is the initial bottom spectra of adjacent categories; s1 and s2 are proportional coefficients, the sum of the two is 1 and s1 is not less than 0.5, and the step size is 0.2. The obtained bottom reflectance sets of high, medium and low categories are respectively expressed as and

[0092] The bottom reflectance is modeled using the above method and expressed as:

[0093]

[0094] Step 5: Construct a multispectral remote sensing uncontrolled water depth inversion algorithm;

[0095] In the multispectral remote sensing uncontrolled water depth inversion algorithm, the remote sensing reflectance of the optical deep water near the target study area is used to obtain the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G, and the nonlinear relationship between the remote sensing reflectance and the bottom parameter B and the water depth H is obtained; the bottom reflectance spectrum is matched pixel by pixel in each region, and the Levenberg-Marquardt optimization algorithm is used to establish the best match between the simulated and actual remote sensing reflectance, and finally the water depth inversion result of the target area is obtained.

[0096] Specifically, the area greater than 30m is selected near the deep water area, the constraint range of parameter C is 0.0001-3, the constraint range of parameter G is 0.0001-0.03, and the constraint range of parameter B is 0.0001-0.8. The optimal water depth is obtained by minimizing the cost function, and the expression is as follows:

[0097]

[0098] Where q is 2, which indicates the number of bands, R rs m(λ i ) is the modeling remote sensing reflectance, R rs (λ i ) represents the actual remote sensing reflectivity.

[0099] In this embodiment, the values ​​of the remote sensing reflectivity calculation parameters C and G of the adjacent deep water area greater than 30m are taken, and the blue band and the green band are selected as the bands. The formula is as follows:

[0100]

[0101]

[0102] a(λ)=a w (λ)+[a0(λ)+a1(λ)ln(P)]P+Ge -S(λ-440) ;

[0103]

[0104] P = 0.06C 0.65 ;

[0105] X=0.0111C 0.62 ;

[0106] in, is the optical deep water subsurface remote sensing reflectivity; u(λ) is the intrinsic optical parameter of the water body; g0 and g1 are empirical parameters. For Class 1 water bodies, g0≈0.0949, g1≈0.0794; a(λ) and b b (λ) are the total absorption coefficient and backscattering coefficient of water body respectively; a w (λ) and b bw (λ) are the pure water absorption coefficient and pure water backscattering coefficient respectively; a0(λ) and a1(λ) are empirical coefficients; P is the phytoplankton absorption coefficient at 550nm; G is the yellow matter absorption coefficient at 550nm; X is the suspended particle absorption coefficient at 550nm; C is the chlorophyll a concentration at 550nm. The constraint range of parameter C is 0.0001~3, and the constraint range of parameter G is 0.0001~0.03.

[0107] Using the relationship between the remote sensing reflectivity of the target area and the bottom parameters B and water depth H, the blue band and the green band are selected, and the expression is as follows:

[0108]

[0109]

[0110]

[0111]

[0112] K(λ)=a(λ)+b b (λ);

[0113] Among them, R rs m (λ) is the modeled remote sensing reflectance; r rs m(λ) is the remote sensing reflectivity of the subsurface of the modeled water body; θ w is the subsurface solar zenith angle; K(λ) is the diffusion attenuation coefficient; is the optical deep-water subsurface remote sensing reflectivity; and is the optical path extension coefficient of water and bottom scattering. The optimal water depth is obtained by matching the bottom spectral value pixel by pixel using the Levenberg-Marquardt optimization algorithm. The original image of Sentinel-2 and the inversion results of shallow water depth are shown in the figure. Figure 3 shown.

[0114] Step 6: Example test to verify model accuracy:

[0115] In this embodiment, the Sentinel-2L1C level remote sensing image data and radar measured water depth point data of a certain area (measured water depth data after preprocessing in step 2) are used as the basis, and then the water depth result is obtained by using the uncontrolled multi-spectral remote sensing water depth inversion algorithm based on pixel-by-pixel matching of the bottom spectrum for testing. The comparison scatter plot of the water depth estimation value and the water depth measured value in this embodiment is shown in the figure. Figure 4 shown.

[0116] The accuracy evaluation indicators used include mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE) and correlation coefficient (R 2 ), the specific formula is as follows:

[0117]

[0118]

[0119]

[0120]

[0121] Where v is the number of measured water depth data; H i H is the measured water depth; i 'To predict water depth; is the average value of the predicted water depth.

[0122] Step 7: Create a continuous water depth map, input the remote sensing reflectivity of the target area's optical shallow water area, obtain the optimal water depth corresponding to each pixel, and output a continuous water depth diagram.

[0123] The uncontrolled multi-spectral remote sensing water depth inversion method in this embodiment uses a depth invariance index to classify the bottom of the target area, and constructs a corresponding bottom spectrum for pixels with bottom spectrum variation, thereby reducing the inversion error caused by the problem of bottom reflectance spectrum variation. Better water depth inversion accuracy can be obtained by using two visible light bands without the need for measured water depth data.

[0124] Corresponding to the uncontrolled multi-spectral remote sensing water depth inversion method in the above embodiment, this embodiment also provides an uncontrolled multi-spectral remote sensing water depth inversion system based on pixel-by-pixel matching of bottom sediment spectra, which includes a preprocessing module, a bottom sediment processing module and an inversion water depth module; wherein:

[0125] The preprocessing module is used to perform atmospheric correction, glare correction and water-land separation on the input remote sensing image data; it is also used to perform tidal correction and data registration on the measured water depth;

[0126] The bottom processing module is used to calculate the depth invariance index of the remote sensing image, obtain a grayscale image representing the size of the bottom reflectance, and divide the bottom reflectance into three categories: high, medium and low, and construct a set of bottom reflectance spectra for each category;

[0127] The inversion water depth module is used to input the constructed bottom reflectance spectrum set into the inversion algorithm to calculate the water depth, including: using the remote sensing reflectivity of the adjacent optical deep water area to obtain the parameters C and G, using the optical shallow water remote sensing reflectivity to match the bottom spectrum information pixel by pixel to obtain the water depth value; verifying the model accuracy; and continuous water depth mapping.

[0128] As for the system in the embodiment of the present invention, since it corresponds to the method in the above embodiment, the description is relatively simple. For the relevant similarities, please refer to the description of the method part in the above embodiment, which will not be described in detail here.

[0129] The uncontrolled multi-spectral remote sensing water depth inversion system in this embodiment uses a depth invariant index to classify the bottom of the target area, and constructs a corresponding bottom spectrum for pixels with bottom spectrum variation, thereby reducing the inversion error caused by the problem of bottom reflectance spectrum variation. Better water depth inversion accuracy can be obtained by using two visible light bands without the need for actual measured water depth data.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An uncontrolled multispectral remote sensing water depth inversion method, characterized in that: The method comprises: Preprocess multispectral satellite remote sensing data; For the pre-processed multispectral satellite remote sensing data, the grayscale map of the bottom reflectance is obtained based on the depth invariance index, and the target area is divided into three types of bottom reflectance areas: high, medium and low. Construct a collection of bottom spectra for each type of bottom reflectance area; The optimized water depth is obtained by using a multispectral remote sensing uncontrolled water depth inversion algorithm. In the multispectral remote sensing uncontrolled water depth inversion algorithm, the remote sensing reflectivity of the optical deep water near the target research area is used to obtain the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G, and the nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and the water depth H is obtained; the bottom reflectance spectrum is matched pixel by pixel in each region, and the Levenberg-Marquardt optimization algorithm is used to establish the best match between the simulated and actual remote sensing reflectivity, and finally the water depth inversion result of the target area is obtained.

2. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The method further comprises: Preprocess the measured water depth data; The water depth result obtained by the uncontrolled multispectral remote sensing water depth inversion algorithm is tested based on the preprocessed measured water depth data to verify the accuracy of the uncontrolled multispectral remote sensing water depth inversion algorithm.

3. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The method further comprises: Create a continuous water depth map, input the remote sensing reflectance of the target area's optical shallow water area, obtain the optimal water depth corresponding to each pixel, and output a continuous water depth diagram.

4. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The preprocessing of multispectral satellite remote sensing data includes: atmospheric correction, glare correction and land-water separation.

5. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The preprocessing of measured water depth data includes: matching the spatial resolution of measured water depth data with the spatial resolution of remote sensing images, and tidal correction.

6. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: Based on the depth invariance index, a grayscale image of the bottom reflectance is obtained, and the target area is divided into three types of bottom reflectance areas: high, medium and low, including: Select the blue band and the green band to calculate the depth invariance index Y and obtain the corresponding grayscale image. The calculation formula of the depth invariance index Y is: Among them, i and j are blue band and green band; L i is the radiance value of band i; L si is the optical deep water i-band radiance value that is not affected by the water surface and atmosphere; K i and K j is the diffusion attenuation coefficient of the water body in band i and band j, which is calculated by the following formula: Among them, X i =ln(L i -L si ), L i L is the i-band optical shallow water radiance in the area with varying depth but uniform bottom quality; si is the optical deep-water radiance in band i. Similarly, X j is the corresponding value of band j; σ ii Yes X i The variance, σ jj Yes X j The variance, σ ij Yes X i and X j The covariance of The optimal threshold is obtained by using Otsu's threshold segmentation method. Two optimal thresholds are obtained by using Otsu's threshold segmentation method on the upper and lower parts of the threshold respectively. Based on these two optimal thresholds, three-partition images with high, medium and low reflectivity are obtained.

7. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 6 is characterized in that: Construct a collection of bottom spectra for each type of bottom reflectance area, including: The representative bottom spectra in the study area are divided into two categories: high and low reflectance. The mean of the bottom spectra of the high reflectance category is used as the initial spectrum of the high reflectance area, the mean of the bottom spectra of the low reflectance category is used as the initial spectrum of the low reflectance area, and the mean of all the spectra representing the bottom is used as the initial spectrum of the medium reflectance area. The bottom spectrum set of each category includes: the typical bottom type reflectance spectrum in the current partition; for the pixels with bottom spectrum variation in the current partition, a mixed spectrum is established: Where λ is the wavelength; ρ' sd (λ) is the set of bottom sediment spectra affected by the bottom sediments of adjacent partitions; is the initial substrate spectrum of the current category; is the initial bottom spectra of adjacent categories; s1 and s2 are proportional coefficients, the sum of the two is 1 and s1 is not less than 0.5, and the step size is 0.2; the obtained high, medium and low bottom reflectance sets are respectively expressed as and The bottom reflectance is modeled using the above method and expressed as: Where, ρ(λ) is the substrate reflectivity; B is the substrate reflectivity at 550nm.

8. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The nonlinear relationship between the subsurface remote sensing reflectivity near the optical deep water area and the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G is: a(λ)=a w (λ)+[a0(λ)+a1(λ)ln(P)]P+Ge -S(λ-440) ; P=0.06C 0.65 ; X=0.0111C 0.62 ; in, is the optical deep water subsurface remote sensing reflectivity; u(λ) is the intrinsic optical parameter of the water body; g0 and g1 are empirical parameters. For Class 1 water bodies, g0≈0.0949, g1≈0.0794; a(λ) and b b (λ) are the total absorption coefficient and backscattering coefficient of water body respectively; a w (λ) and b bw (λ) are the pure water absorption coefficient and pure water backscattering coefficient respectively; a0(λ) and a1(λ) are empirical coefficients; P is the phytoplankton absorption coefficient at 550nm; G is the yellow matter absorption coefficient at 550nm; X is the suspended particle absorption coefficient at 550nm; C is the chlorophyll a concentration at 550nm.

9. The uncontrolled multi-spectral remote sensing water depth inversion method according to claim 1, characterized in that: The nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and water depth H is: K(λ)=a(λ)+b b (l); Among them, R rs m (λ) is the modeled remote sensing reflectance; r rs m (λ) is the remote sensing reflectivity of the modeled water subsurface; θ w is the subsurface solar zenith angle; K(λ) is the diffusion attenuation coefficient; is the optical deep-water subsurface remote sensing reflectivity; and is the optical path extension coefficient for water and bottom scattering.

10. An uncontrolled multi-spectral remote sensing water depth inversion system, characterized in that: The system comprises: The preprocessing module is used to perform atmospheric correction, glare correction and water-land separation on the input remote sensing image data; it is also used to perform tidal correction and data registration on the measured water depth; The bottom processing module is used to calculate the depth invariance index of the pre-processed multispectral satellite remote sensing data, obtain a grayscale image representing the size of the bottom reflectance, and divide it into three categories: high, medium and low according to the level of bottom reflectance, and construct a set of bottom reflectance spectra for each category; The inversion water depth module is used to obtain the optimized water depth using a multispectral remote sensing uncontrolled water depth inversion algorithm. In the multispectral remote sensing uncontrolled water depth inversion algorithm, the remote sensing reflectivity of the optical deep water near the target research area is used to obtain the chlorophyll a concentration parameter C and the yellow matter absorption coefficient parameter G, and the nonlinear relationship between the remote sensing reflectivity and the bottom parameter B and the water depth H is obtained; the bottom reflectance spectrum is matched pixel by pixel in each region, and the Levenberg-Marquardt optimization algorithm is used to establish the best match between the simulated and actual remote sensing reflectivity, and finally the water depth inversion result of the target area is obtained.

Images