Multispectral remote sensing image water depth inversion method based on chart data, medium and equipment

Abstract

The present application belongs to a kind of multispectral remote sensing image water depth retrieval method, to solve the technical problems that traditional multispectral and hyperspectral water depth retrieval method cannot be quickly and effectively promoted to all same type optical remote sensing load in the case where some parameter information is missing or inaccurate, provide a kind of multispectral remote sensing image water depth retrieval method based on chart data, computer readable storage medium, terminal equipment, construct supervised water depth retrieval model, with the discrete water depth point of chart easy to obtain as supervision data, regression training model parameter, and applied to whole scene remote sensing image, can realize the water depth retrieval of optical remote sensing data not dependent on absolute radiation, atmosphere, water, bottom material and other parameter information, so as to greatly increase the application range of the water depth retrieval method.

Description

TECHNICAL FIELD

[0001] The application belongs to a multispectral remote sensing image water depth inversion method, and particularly relates to a multispectral remote sensing image water depth inversion method based on chart data, a computer readable storage medium and a terminal device. BACKGROUND

[0002] Water depth is an important topographic element of shallow sea, and is of great significance to maritime traffic and shipping, coastal engineering development, island and coastal zone management. The water depth and underwater topography around the coastal zone and islands are also important contents of the informationization construction of sea battlefield. The water depth of the coastal zone determines the navigation and activity range of surface ships in the nearshore sea area, and is the main basis for laying mine types and positions. Optical remote sensing is the main way of water depth remote sensing detection. According to different detection methods, it can be divided into two categories: passive optical remote sensing and active optical remote sensing. Passive optical remote sensing uses sunlight as the light source, and collects nearshore water body radiation information through multispectral sensors and hyperspectral sensors to perform water depth inversion. Active optical remote sensing uses active laser as the light source, and collects water surface and water bottom echo signals according to the radar principle to perform water depth inversion. Passive optical remote sensing has become the most important optical remote sensing water depth inversion method due to its wide observation range and high spatial resolution.

[0003] Scholars have been concerned about water depth remote sensing technology since the 1960s. With the successful launch of remote sensing satellites, the model method for inverting water depth using multispectral satellite remote sensing data has also developed rapidly, mainly forming three forms: theoretical analysis model, semi-theoretical and semi-empirical model, and statistical model. The theoretical analysis model is based on the water light field radiation transfer equation, and an analytical expression of the radiance received by the optical remote sensor and the water depth and bottom reflectance is established, and then the water depth is calculated through the expression. The semi-theoretical and semi-empirical model realizes passive optical remote sensing water depth inversion by combining theoretical models and empirical parameters. The log-linear model is the most widely used semi-theoretical and semi-analytical model. The water depth inversion model obtained by directly establishing the statistical relationship between the remote sensing image radiance value and the measured water depth value is collectively referred to as a statistical model, and the expression mainly includes power function, logarithmic function and linear model. High spectral remote sensing has the characteristics of "graph and spectrum integration", which can not only obtain spatial information of ground objects, but also record spectral information of ground objects. High spectral remote sensing has rich spectral information, and is a hot spot and frontier of water depth remote sensing inversion research in recent years. High spectral water depth inversion models mainly include lookup table method, spectral differential statistical model, neural network model and semi-analytical model, etc.

[0004] Traditional multispectral and hyperspectral water depth inversion methods simulate the absorption, scattering, reflection and other radiation attenuation processes of solar radiation energy through the atmosphere, water surface, water body, water bottom and other media, thereby inverting the water depth, and have strong theoretical basis and high inversion accuracy. However, due to the need for atmospheric, water quality, and bottom information, the applicability is limited, and in the case of missing or inaccurate parameter information, it cannot be quickly and effectively promoted to all types of optical remote sensing loads, and thus cannot be widely applied. SUMMARY

[0005] To solve the technical problem that the traditional multispectral and hyperspectral water depth inversion method cannot be quickly and effectively promoted to all types of optical remote sensing loads in the case of missing or inaccurate parameter information, a multispectral remote sensing image water depth inversion method based on chart data, a computer readable storage medium, and a terminal device are provided.

[0006] To achieve the above-mentioned purpose, the following technical solutions are adopted:

[0007] The multispectral remote sensing image water depth inversion method based on chart data is characterized in that it comprises the following steps:

[0008] S1, extracting a water body region from a multispectral remote sensing image to obtain a water body region multispectral remote sensing image; the multispectral remote sensing image comprises at least four wave bands corresponding to blue light, green light, red light, and near-infrared light;

[0009] S2, performing wave-by-wave sea wave correction on the water body region multispectral remote sensing image to obtain a corrected water body region multispectral remote sensing image; removing a reef region to obtain a water body region reef-removed multispectral remote sensing image;

[0010] S3, removing the reef region from the corrected water body region multispectral remote sensing image to obtain a water body region reef-removed multispectral remote sensing image;

[0011] S4, extracting depth information of discrete water depth points in chart data, and extracting spectral data of corresponding position points in the water body region reef-removed multispectral remote sensing image according to the latitude and longitude coordinates of the discrete water depth points;

[0012] S5, constructing a wave band ratio water depth inversion model according to the depth information of the discrete water depth points and the spectral data of the corresponding position points obtained in step S4;

[0013] S6, performing water depth inversion on the water body region reef-removed multispectral remote sensing image pixel by pixel through the wave band ratio water depth inversion model to obtain a preliminary water depth inversion result;

[0014] S7, performing mask on deep water areas in the preliminary water depth inversion result by threshold method to obtain water depth image data.

[0015] Further, before step S1, the method further comprises step S0 of performing geometric correction on the multi-spectral remote sensing image and the chart data.

[0016] Further, step S1 specifically comprises:

[0017] S1.1, calculating normalized difference water index NDWI of the multi-spectral remote sensing image pixel by pixel through the following formula:

[0018]

[0019] wherein, P Blue represents blue band data in the multi-spectral remote sensing image, P NIR represents near-infrared band data in the multi-spectral remote sensing image.

[0020] S1.2, setting the NDWI threshold of the current water body, taking the NDWI greater than the NDWI threshold in each pixel of the multi-spectral remote sensing image as the water body region, and obtaining the water body region multi-spectral remote sensing image.

[0021] Further, in step S2, wave correction is performed through the following formula:

[0022] P λ1 = P λ -k λ (P NIR -min(P NIR ))

[0023] wherein, P λ1 represents blue, green or red band data in the corrected multi-spectral remote sensing image, P λ represents blue, green or red band data in the multi-spectral remote sensing image, P NIR represents near-infrared band data, min(P NIR ) represents the minimum value of the near-infrared band data in the current region, and k λ represents a correction coefficient.

[0024] Further, the correction coefficient k λ is obtained through the following method:

[0025] From a deep water region selected from the water body region multi-spectral remote sensing image, a precision step is set, and all values between 0 and 2 are traversed according to the precision step to obtain a variance corresponding to the deep water region, a minimum value of the variance is determined, and a value between 0 and 2 corresponding to the minimum value of the variance is taken as the correction coefficient k λ .

[0026] Further, step S3 specifically comprises:

[0027] S3.1, a reef determination value Reef is calculated by the following formula:

[0028] Reef=P Green -P Blue

[0029] Wherein, P Green represents the green light band data in the multispectral remote sensing image;

[0030] S3.2, if the Reef is greater than zero, the corresponding position in the multispectral remote sensing image is taken as a reef area, otherwise, it is taken as a non-reef area; the reef area is removed from the corrected multispectral remote sensing image of the water area to obtain a multispectral remote sensing image of the water area without reef.

[0031] Further, step S5 is specifically:

[0032] S5.1, a band ratio water depth inversion model is established as follows:

[0033]

[0034] Wherein, Depth represents the water depth value, P Red represents the red light band data in the multispectral remote sensing image, a, b and c respectively represent the first parameter, the second parameter and the third parameter of the band ratio water depth inversion model;

[0035] S5.2, the depth information of a plurality of discrete water depth points and the spectral data of the corresponding position points are used to determine the first parameter a, the second parameter b and the third parameter c by using the least square method;

[0036] S5.3, the band ratio water depth inversion model is obtained.

[0037] Meanwhile, the application also provides a computer readable storage medium, which stores a computer program, and the special feature is that the program is executed by the processor to realize the steps of the multispectral remote sensing image water depth inversion method based on the chart data.

[0038] In addition, the application also provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the special feature is that the processor executes the computer program to realize the steps of the multispectral remote sensing image water depth inversion method based on the chart data.

[0039] Compared with the prior art, the application has the following beneficial effects:

[0040] 1.The method for quickly retrieving water depth from multispectral remote sensing images based on chart data, constructs a supervised water depth retrieval model, uses the discrete water depth points of the easily obtained chart as supervision data, regresses and trains the model parameters, and applies them to the whole remote sensing image; compared with the existing multispectral or hyperspectral water depth retrieval algorithm, the method does not need to perform preprocessing operations such as absolute radiation correction and atmospheric correction on the original remote sensing image, and does not need parameter information such as seawater quality and seabed bottom, and thus the water depth retrieval from optical remote sensing data can be realized without dependence on absolute radiation, atmosphere, water body, and bottom, thereby greatly increasing the application range of the water depth retrieval method.

[0041] 2.The water land separation algorithm based on the normalized water index (NDWI) is used to quickly and effectively extract the water area, the sea wave removal algorithm based on the near-infrared band correction can effectively eliminate the influence of the sun glint reflected by the sea wave on the water depth retrieval accuracy, the reef removal method based on the blue-green band ratio method can effectively remove the reef area in the water body that affects navigation, and thus the water depth retrieval method of the present application is more efficient and accurate.

[0042] 3.The water depth retrieval method can be used for different local areas, and the model parameters of the respective areas are regressed and trained by using the chart discrete water depth points of the areas, and thus the water depth retrieval accuracy can reach the optimal value of the local area due to the high uniformity of the atmosphere, water body, and bottom parameters of the local area. In addition, the water depth retrieval method uses a linear regression model, and thus has high running efficiency and high robustness.

[0043] 4.The present application also provides a computer readable storage medium and a terminal device capable of executing the above method steps, and the method of the present application can be popularized and applied to realize fusion on the corresponding hardware device. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 It is a flowchart of the embodiment of the method for quickly retrieving water depth from multispectral remote sensing images based on chart data of the present application;

[0045] Figure 2 It is a multispectral remote sensing image in the embodiment of the present application;

[0046] Figure 3 It is chart data in the embodiment of the present application;

[0047] Figure 4 It is the band data of various lights in the multispectral remote sensing image in the embodiment of the present application; wherein (a) is the band data of blue light, (b) is the band data of green light, (c) is the band data of red light, and (d) is the band data of near-infrared light;

[0048] Figure 5 It is an NDWI value image in the embodiment of the present application;

[0049] Figure 6 Fig. 1 is a schematic diagram of water body region extraction results in an embodiment of the present application;

[0050] Figure 7 Fig. 2 is a schematic diagram of wave correction in each wave band in an embodiment of the present application;

[0051] Figure 8 Fig. 3 is a curve diagram of water depth values of each water depth point in actual measurement and inversion of chart data in an embodiment of the present application;

[0052] Figure 9 Fig. 4 is a preliminary water depth inversion result obtained in an embodiment of the present application;

[0053] Figure 10 Fig. 5 is a schematic diagram of water depth image data finally obtained by masking deep water area through threshold method in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments but not all of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0055] The present application can promote large-scale practical application of optical remote sensing water depth inversion by means of supervised water depth inversion with fusion of other easily obtained multi-source data such as chart data. Figure 1 As shown in the figure, the water depth inversion method of the present application includes the following steps:

[0056] S1, extracting water body region from data of multi-spectral remote sensing image

[0057] Firstly, the present application needs data of multi-spectral remote sensing image and chart data. The input multi-spectral remote sensing data in the present application needs to contain at least four wave bands of blue, green, red and near-infrared, and needs to be geometrically corrected, each pixel point has latitude and longitude coordinate information, and does not need to be absolutely radiometrically corrected and atmospherically corrected. The input chart data of the corresponding region needs to contain discrete actual measurement water depth points, and also needs to be geometrically corrected, i.e. each water depth point has latitude and longitude coordinate information.

[0058] Then, the multi-spectral remote sensing image data is subjected to water body region extraction by NDWI index method, wherein NDWI (Normalized Difference Water Index) refers to normalized water index, and the calculation formula is as follows:

[0059]

[0060] wherein P Blue is the blue band data of the multispectral remote sensing image, P NIR is the near-infrared band data of the multispectral remote sensing image. The NDWI is calculated pixel by pixel for the multispectral remote sensing image, and the region with an NDWI value greater than a threshold value Tw is the water body region, wherein Tw is an empirical parameter and can be set according to the actual situation of the current water body, and is generally set to 0.5.

[0061] S2, wave correction is performed on the water body region extracted in step S1 wave by wave, to eliminate the influence of the uniformity of the sun glint on the water body radiation

[0062] Wave correction is performed on the water body region wave by wave, to eliminate the influence of the uniformity of the sun glint on the water body radiation, and the near-infrared band correction method is used to correct the influence of the sun glint caused by the wave on the blue, green and red bands, and the correction formula is as follows:

[0063] P λ1 = P λ -k λ (P NIR -min(P NIR ))

[0064] wherein P λ1 represents the corrected blue, green or red band data, P λ represents the blue, green or red band data, P NIR represents the near-infrared band data, and min(P NIR ) is the minimum value of the near-infrared band data of the current region. k λ is a correction coefficient, and the correction coefficients of the blue, green and red bands are different. Generally, k λ is in the range of 0-2, and the accurate solving method is to select a deep water region in the multispectral remote sensing image, and the blue, green and red band data of the deep water region after wave correction should be uniformly distributed, i.e., the variance is minimum, so that the precision step (such as 0.01) is set, and k λ is obtained by traversing all values before 0-2 to make the variance of each band of the deep water region minimum, which is the optimal k λ for each band.

[0065] S3, the rock region affecting navigation in the water body is removed from the multispectral remote sensing image after wave correction wave by wave in step S2

[0066] The rock region affecting navigation in the water body is removed, and the blue-green band ratio method is used, i.e., the region with a P Green value greater than Tr×P Blue value is considered as a rock region, and the following formula is used to determine whether the rock determination value Reef is greater than zero, i.e., the P Green value and Tr×PBlue The size relationship between Tr and T is that Tr is generally set as 1 as an experience parameter.

[0067] Reef = P Green -T r P Blue .

[0068] S4, extracting depth information of discrete water depth points in the chart data and extracting spectral information of corresponding position points in the multispectral remote sensing image

[0069] Since the multispectral remote sensing data and the chart data are both geometrically corrected, that is, each pixel point position has latitude and longitude coordinate information, the depth information of the discrete water depth points in the chart is extracted, and the spectral data of the corresponding position points in the multispectral remote sensing image is extracted according to the latitude and longitude coordinates of the water depth points, as the subsequent model training data.

[0070] S5, constructing a band ratio water depth inversion model

[0071] The band ratio water depth inversion model is constructed by using the fact that the energy of the sunlight radiated underwater decays exponentially and the decay coefficients of different bands are different, as follows:

[0072]

[0073] wherein, Depth is the water depth value, P Blue , P Green , P Red are respectively the blue, green and red band values in the multispectral remote sensing image, and a, b and c are all parameters which can be solved by regression using the chart water depth point data and the corresponding spectral data.

[0074] S6, solving the model parameters by using the least square method

[0075] According to the N groups of chart water depth point data and the corresponding spectral data, the band ratio water depth inversion model parameters are solved by using the least square method, and the calculation formula is as follows:

[0076] θ = (X T X) -1 X T Y

[0077] θ = [a b c] T

[0078]

[0079]

[0080] wherein, θ is the coefficient vector to be solved by regression in the water depth inversion model, X is the N groups of water depth point multispectral band ratio value data matrix, Y is the N groups of water depth point water depth data, PBlue P2 is the blue band data of the first group of multi-spectral remote sensing images B1ue Pn is the blue band data of the nth group of multi-spectral remote sensing images, and the like Blue P2 is the blue band data of the second group of multi-spectral remote sensing images, and correspondingly, the subscript Green represents the green band data, the subscript Red represents the red band data, Depth1 is the water depth data of the first group of water depth points, and DepthN is the water depth data of the nth group of water depth points.

[0081] S7, water depth inversion is performed on the multi-spectral remote sensing image pixel by pixel

[0082] Based on the band ratio water depth inversion model, the parameters obtained by the regression in the previous step are substituted to perform water depth inversion on the whole multi-spectral remote sensing image pixel by pixel.

[0083] S8, the threshold method is used to mask the deep water area to finally obtain the water depth image data.

[0084] The threshold method is used to mask the deep water area to finally obtain the water depth image data. Since multi-spectral remote sensing water depth inversion requires sunlight to reach the seabed and be reflected out of the water surface, the deep water area cannot be inverted by optical remote sensing method in principle, that is, the band ratio water depth inversion model is not applicable to the deep water area, and therefore a threshold Td is set to mask the deep water area. The threshold Td is set according to the water quality experience, for example, the Td value of a clear ocean type of water body is 30 meters.

[0085] The following is a specific embodiment of the water depth inversion method of the present application:

[0086] As shown in Figure 2 and Figure 3 , a certain island region in China is selected, and the water depth inversion method of the present application is used to perform water depth inversion experiment, to obtain multi-spectral remote sensing images and corresponding chart data of the region, wherein the multi-spectral remote sensing images include blue, green, red and near-infrared four band data, as shown in Figure 4 . As shown in Figure 5 and Figure 6 , the NDWI index method is used to extract the water body area from the multi-spectral remote sensing image data. First, the NDWI value of the whole multi-spectral remote sensing image is calculated according to the NDWI formula, and the threshold Tw is set to 0.5. When the NDWI value is greater than 0.5, it is considered to be a water body area. As shown in Figure 7 , the water body area is corrected wave by wave, the influence of sun glint on the radiation uniformity of the water body is eliminated, and the near-infrared band correction method is used to correct the influence of sun glint caused by the sea wave on the other three bands of blue, green and red. In this example, the correction coefficient k λ corresponding to the optimal value of the blue light is 0.5, the correction coefficient k blue corresponding to the optimal value of the green light is 0.5, and the correction coefficient k greenThe optimal value of the red light corresponding correction coefficient k red The optimal value of the red light corresponding correction coefficient k Green The area with the value greater than P Blue The area with the value greater than P Figure 8 The area with the value greater than P Figure 8 The area with the value greater than P Figure 9 The area with the value greater than P Figure 10 The area with the value greater than P

[0087] The water depth inversion method of the present application can be applied in a computer readable storage medium, the computer readable storage medium stores a computer program, the above water depth inversion method can be stored in the computer readable storage medium as a computer program, and each step of the above water depth inversion method is realized when the computer program is executed by a processor.

[0088] In addition, the water depth inversion method of the present application can also be applied to a terminal device, the terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor realizes the steps of the water depth inversion method of the present application when executing the computer program. The terminal device here can be a computer, a notebook, a palm computer, and various cloud servers and other computing devices, and the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, or other programmable logic devices.

[0089] The above is only the preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for water depth retrieval from multispectral remote sensing images based on chart data, characterized in that, The method comprises the following steps: S1, extracting a water area from a multi-spectral remote sensing image to obtain a water area multi-spectral remote sensing image; the multi-spectral remote sensing image comprises at least four wave bands corresponding to blue light, green light, red light and near-infrared light; S2, performing wave band by wave band sea wave correction on the water area multi-spectral remote sensing image to obtain a corrected water area multi-spectral remote sensing image; S3, removing a reef area from the corrected water area multi-spectral remote sensing image to obtain a water area reef-removed multi-spectral remote sensing image; S4, extracting depth information of discrete water depth points in chart data, and extracting spectral data of corresponding position points in the water area reef-removed multi-spectral remote sensing image according to the longitude and latitude coordinates of the discrete water depth points; S5, constructing a wave band ratio water depth inversion model according to the depth information of the discrete water depth points and the spectral data of the corresponding position points obtained in step S4, specifically as follows: S5.1, constructing a wave band ratio water depth inversion model as follows: wherein, Depth represents water depth value, P Red represents red light band data in multispectral remote sensing image, P Blue represents blue light band data in multispectral remote sensing image, P Green represents green light band data in multispectral remote sensing image, a, b, c respectively represent the first parameter, the second parameter and the third parameter of band ratio water depth inversion model; S5.2, determining the first parameter a, the second parameter b and the third parameter c by using the least square method through a plurality of sets of depth information of the discrete water depth points and the spectral data of the corresponding position points; S5.3, obtaining the wave band ratio water depth inversion model; S6, performing water depth inversion on the water area reef-removed multi-spectral remote sensing image pixel by pixel through the wave band ratio water depth inversion model to obtain a preliminary water depth inversion result; S7, performing mask on a deep water area in the preliminary water depth inversion result by using a threshold method to obtain water depth image data.

2. The method according to claim 1, wherein: Before step S1, there is also a step S0 of performing geometric correction on the multi-spectral remote sensing image and the chart data.

3. The method according to claim 1 or 2, characterized in that, Step S1 is specifically as follows: S1.1, calculating normalized water index NDWI of the multi-spectral remote sensing image pixel by pixel through the following formula: wherein P NIR represents the near-infrared light wave band data in the multispectral remote sensing image; S1.2, setting an NDWI threshold value of a current water body, regarding each pixel in the multi-spectral remote sensing image corresponding to NDWI greater than the NDWI threshold value as a water area to obtain the water area multi-spectral remote sensing image.

4. The method according to claim 3, wherein, In step S2, wave band by wave band sea wave correction is performed through the following formula: P λ1 = P λ - k λ (P NIR - min(P NIR )) Wherein, P λ1 represents the corrected blue, green or red band data in the multispectral remote sensing image, P λ represents the blue, green or red band data in the multispectral remote sensing image, P NIR represents the near-infrared band data, min(P NIR ) represents the minimum value of the near-infrared band data in the current region, k λ represents the correction coefficient.

5. The method according to claim 4, wherein, The correction factor k λ By the following method: From the water area multi-spectral remote sensing image, an optional deep water area is set, an accuracy step is set, all values between 0 and 2 are traversed according to the accuracy step, a variance corresponding to the deep water area is obtained, a minimum value of the variance is determined, and a value between 0 and 2 corresponding to the minimum value of the variance is taken as a correction coefficient k λ .

6. The method according to claim 5, wherein, Step S3 is specifically as follows: S.3.1, calculating a reef determination value Reef through the following formula: Reef = P Green - P Blue S3.2, if the Reef is greater than zero, regarding the corresponding position in the multi-spectral remote sensing image as a reef area, otherwise, regarding the corresponding position as a non-reef area; removing the reef area from the corrected water area multi-spectral remote sensing image to obtain the water area reef-removed multi-spectral remote sensing image.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: The processor executes the computer program to implement the steps of the multi-spectral remote sensing image water depth inversion method based on chart data according to any one of claims 1 to 6.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the multi-spectral remote sensing image water depth inversion method based on chart data according to any one of claims 1 to 6.

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