CN109059796B - Shallow sea water depth multispectral satellite remote sensing inversion method for water depth control point-free area - Google Patents
Shallow sea water depth multispectral satellite remote sensing inversion method for water depth control point-free area Download PDFInfo
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Abstract
A shallow sea water depth multispectral satellite remote sensing inversion method in a no-water-depth control point area belongs to the technical field of satellite ocean remote sensing application. Based on a derived two-waveband linear shallow sea water depth inversion model, an optimal waveband rotation unit vector is solved through pixel point pairs of data sets of different seabed types at different depths, seabed parameters in the model are estimated based on typical substrate type pixel adoption of land and water boundary line positions, meanwhile, a blue-green waveband diffusion attenuation coefficient ratio is obtained through pixel data of the same substrate type at different depths, and a green waveband attenuation coefficient is calculated through a half-analysis and diffusion attenuation coefficient algorithm based on deep water area data of the nearest shallow sea area. And (4) calculating the parameters to realize shallow sea water depth remote sensing inversion of the water-depth-free control point area.
Description
Technical Field
The invention belongs to the technical field of satellite marine remote sensing application, and particularly relates to a shallow sea water depth multispectral satellite remote sensing inversion method in a water depth control point-free area.
Background
The depth of shallow sea water is used as an important parameter for safe navigation guarantee of ships and warships, offshore ecosystems and optical research, and is always an important content of ocean mapping and optical remote sensing. The conventional shallow sea water depth measurement is mainly ship-borne multi/single beam acoustic measurement, and the airborne laser depth measurement technology developed in recent years is gradually widely applied. However, for some dangerous or disputed sea areas, these techniques are time consuming, laborious and even impossible to implement. Although the detection depth and precision cannot replace the conventional ocean measurement, the satellite remote sensing technology is the only feasible method for acquiring shallow sea water depth data in the regions. Therefore, the development of the research on the shallow sea water depth satellite remote sensing inversion technology has important significance and application prospect.
The existing shallow sea water depth satellite remote sensing inversion method can be generally summarized into two categories: the high spectrum remote sensing based on the semi-analytical algorithm and the multi spectrum remote sensing based on the empirical model are basically equivalent in the current water depth inversion accuracy from the literature reports. Although the hyperspectral remote sensing has the advantages of definite physical basis, no need of actually measuring water depth points and the like, the existing hyperspectral image has the defects of low spatial resolution and less available data. In contrast, the spatial resolution of the multispectral image can reach 2m at most, and the number of available satellites is large, so that the multispectral image is more suitable for carrying out shallow sea water depth remote sensing. However, the multispectral shallow sea water depth inversion model requires a certain amount of actually measured or reliable sea chart water depth data as input to carry out model coefficient calculation. Due to the change of the water body property and the seabed type, the multispectral water depth inversion model has obvious regionality. For some remote, dangerous or controversial remote sensing inversion methods, the number of measured water depth points is small, the water depth inversion result is unreliable, and sometimes even no available water depth control point exists, so that the shallow sea water depth remote sensing inversion cannot be carried out. Therefore, it is necessary to develop a shallow sea water depth multispectral satellite remote sensing inversion method without a water depth control point area.
Disclosure of Invention
The invention aims to provide a shallow sea water depth multispectral satellite remote sensing inversion method in a region without a water depth control point by analyzing and expressing a two-waveband linear model and combining multispectral image sampling points to calculate parameters required by the shallow sea water depth model aiming at the difficult problem that the multispectral shallow sea water depth remote sensing inversion requires the water depth control point.
The invention comprises the following steps:
1) deriving a shallow sea water depth inversion formula (1) according to a vector product form of the dual-waveband linear model:
Xi＝ln[rw(λi)-rdp(λi)]i＝1,2
wherein z is the shallow sea depth to be inverted α1,α2Weighting feature vectors of blue-green wave bands; g1,g2The water body double-pass diffusion attenuation coefficient is a blue-green waveband spectrum; r isw(λi) Remote reflectance below the water surface in the ith band (cyan); r isb(λi) The seabed remote sensing reflectivity of the ith wave band (blue green); r isdp(λi) The remote sensing reflectivity under the water surface of the ith wave band (blue green) optical deepwater zone; obtaining a water depth remote sensing inversion result of the water depth point-free area;
2) selecting adjacent pixel pairs with different depths and different substrates on the image, and carrying out minimum solving on an Xi data set of the adjacent pixel pairs to obtain a group of optimal waveband rotation unit vectors [ α ]1,α2]：
Where i denotes a certain adjacent pixel pair, Δ sziA, B shows different substrate types corresponding to the pixel point pair, n is the number of the pixel point pair, and f is a minimization function;
3) selecting a plurality of typical seabed sediment image element sets at the water line of the image, and combining to obtain an optimal waveband rotation unit vector [ α ]1,α2]Obtaining sea bottom parameters by average statistics
A value;
4) using the same type of seabed substrate on the image and X on different depth positions1～X2Data set, calculating blue-green wave band double-pass diffusion attenuation coefficient ratio g1/g2；
5) On the premise of assuming uniform water body properties, calculating the green band diffusion attenuation coefficient g nearest to the optical deepwater zone of the shallow sea area by using a semi-analysis and diffusion attenuation coefficient algorithm2；
6) The coefficients obtained by the calculation of the steps comprise [ α ]1、α2]Subsea parameter
g1/g2And g2Substituting the shallow sea water depth inversion formula, and applying the shallow sea water depth inversion formula to the whole image to realize the shallow sea water depth multispectral satellite remote sensing inversion of the region without the water depth control point.
Compared with the prior art, the invention has the following advantages:
1) the method is based on a derived two-waveband linear shallow sea water depth inversion model, an optimal waveband rotation unit vector is solved through pixel point pairs of data sets of different seabed types at different depths, seabed parameters in the model are estimated based on typical substrate type pixel adoption of land and water boundary line positions, meanwhile, a blue-green waveband diffusion attenuation coefficient ratio is obtained through pixel data of the same substrate type at different depths, a deep water region data of the nearest shallow sea region is based, and a green waveband attenuation coefficient is calculated through a half-analysis and diffusion attenuation coefficient algorithm. And (4) calculating the parameters to realize shallow sea water depth remote sensing inversion of the water-depth-free control point area.
2) Compared with the existing water depth inversion method which needs the participation of water depth control points, the method can eliminate the influence of the type difference of the seabed sediment on the water depth inversion to a certain extent, and can realize the shallow sea water depth remote sensing inversion of the area without the water depth control points.
Drawings
FIG. 1 is a distribution diagram of image pixel sampling points, water depth precision verification points and the like in the embodiment of the invention;
FIG. 2 shows a blue and green band sandy seabed sampling point X in the embodiment of the invention1～X2Scatter plot and its linear fit;
FIG. 3 is a water depth remote sensing inversion result diagram in the embodiment of the invention;
FIG. 4 is a result of verifying the inversion water depth accuracy of the new method in the embodiment of the present invention.
Detailed Description
According to the method, aiming at the water depth multispectral satellite remote sensing of the water depth in the no-water-depth point area, relevant parameters required by water depth inversion are obtained through the image, and therefore shallow-sea water depth remote sensing inversion of the no-water-depth point area is achieved.
The following describes in detail the specific implementation process of the technical solution of the present invention with reference to the accompanying drawings and examples:
the method comprises the following steps: acquiring a high-resolution multispectral satellite image of a research area, carrying out atmospheric correction to obtain a reflectivity image, and converting reflectivity data into underwater remote sensing reflectivity data rw. When the satellite image positioning error is lower than 6m, geometric correction processing needs to be carried out firstly; when the image has obvious flare interference, flare correction processing is required to be carried out;
step two: through visual interpretation, selecting the pixels (figure 1) of the deep water area nearest to the shallow sea area, and counting the blue and green bands rdpValue and according to formula Xi＝ln[rw(λi)-rdp(λi)]Calculating X of blue and green wave bandiImage data;
selecting adjacent pixel point pairs of different substrate types from different distances (representing different depths) along the shore line to obtain a data set (figure 1), and calculating by using an optimal algorithm (formula 2) to obtain an optimal waveband rotation unit vector [ α ]1,α2]；
Step four: referring to a near infrared image, selecting a pixel set (figure 1) of a typical substrate type on an amphibious boundary line of the image, and calculating XiData, and the obtained optimal band rotation unit vector [ α ]1,α2]Calculating the sea floor parameters
Step five: x for selecting positions of sandy seabed and different depths on image1～X2Establishing a linear regression formula (shown as figure 2) of the data set (figure 1) and the data set by using a least square method to obtain a ratio g of the two-way diffusion attenuation coefficients of the water body in blue and green wave bands1/g2；
Step six: according to a semi-analysis and diffusion attenuation coefficient algorithm, calculating the sum g of upward and downward diffusion attenuation coefficients of green wave bands of pixels in a deep water area nearest to the shallow sea area2；
Step seven, calculating the obtained water-free deep point region [ α1、α2]、
g1/g2And g2Substituting the parameters into a formula 1, and applying the parameters to the whole image to obtain a water depth remote sensing inversion result of the water depth-free area (as shown in FIG. 3);
step eight: randomly selecting an actual measurement water depth data set (distribution is shown in figure 1) to carry out precision verification on the water depth remote sensing inversion result, drawing a scatter diagram for comparing the inversion water depth with the actual measurement water depth, and calculating a root mean square error RMSE (shown in figure 4). In this example, the RMSE error for the water depth inversion is 1.18 m.
Claims (1)
1. The shallow sea water depth multispectral satellite remote sensing inversion method of the no water depth control point area is characterized by comprising the following steps:
1) deriving a shallow sea water depth inversion formula according to a vector product form of the dual-waveband linear model:
Xi＝ln[rw(λi)-rdp(λi)]i＝1,2
wherein z is the shallow sea depth to be inverted α1,α2Weighting feature vectors of blue-green wave bands; g1,g2The water body double-pass diffusion attenuation coefficient is a blue-green waveband spectrum; r isw(λi) The remote sensing reflectivity under the water surface of the ith wave band; r isb(λi) The seabed remote sensing reflectivity is the ith wave band; r isdp(λi) The remote sensing reflectivity under the water surface of the i-band optical deep water area is obtained; obtaining a water depth remote sensing inversion result of the water depth point-free area;
2) selecting adjacent pixel pairs with different depths and different substrates on the image, and carrying out minimum solving on an Xi data set of the adjacent pixel pairs to obtain a group of optimal waveband rotation unit vectors [ α ]1,α2]：
Where i denotes a certain adjacent pixel pair, Δ sziA, B shows different substrate types corresponding to the pixel point pair, n is the number of the pixel point pair, and f is a minimization function;
3) selecting a plurality of typical seabed sediment image element sets at the water line of the image, and combining to obtain an optimal waveband rotation unit vector [ α ]1,α2]Obtaining sea bottom parameters by average statistics
A value;
4) using the same type of seabed substrate on the image and X on different depth positions1～X2Data set, calculating blue-green wave band double-pass diffusion attenuation coefficient ratio g1/g2；
5) On the premise of assuming uniform water body properties, calculating the green band diffusion attenuation coefficient g nearest to the optical deepwater zone of the shallow sea area by using a semi-analysis and diffusion attenuation coefficient algorithm2；
6) The coefficients obtained by the calculation of the steps comprise [ α ]1、α2]Subsea parameter
g1/g2And g2Substituting the shallow sea water depth inversion formula, and applying the shallow sea water depth inversion formula to the whole image to realize the shallow sea water depth multispectral satellite remote sensing inversion of the region without the water depth control point.
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| CN114758254B (en) * | 2022-06-15 | 2022-11-18 | 中国地质大学（武汉） | Dual-band unsupervised water depth inversion method and system |
| CN115422981B (en) * | 2022-11-04 | 2023-04-07 | 自然资源部第一海洋研究所 | Land and water classification method and system for single-frequency airborne laser sounding data and application |
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