CN115856925A - Multispectral remote sensing image water depth inversion method, medium and equipment based on chart data - Google Patents
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Abstract
The invention belongs to a multispectral remote sensing image water depth inversion method, and aims to solve the technical problem that the conventional multispectral and hyperspectral water depth inversion methods cannot be rapidly and effectively popularized to all optical remote sensing loads of the same type under the condition that some parameter information is missing or inaccurate.
Description
Technical Field
The invention 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 sea chart data, a computer readable storage medium and terminal equipment.
Background
The water depth is an important topographic factor of shallow sea, and has important significance for marine traffic shipping, coastal engineering development and island coastal zone management, the water depth and underwater topography around coastal zones and island reefs are also important contents for the information construction of sea battlefield, and the water depth of the coastal zones determines the navigation and the activity range of surface naval vessels in the coastal sea area and is a main basis for laying the type and the position of mines. Optical remote sensing is a main mode for water depth remote sensing detection, and can be divided into two categories, namely passive optical remote sensing and active optical remote sensing according to different detection modes. The passive optical remote sensing utilizes sunlight as a light source, and collects near-shore water body radiation information through a multispectral sensor and a hyperspectral sensor so as to perform water depth inversion; active optical remote sensing utilizes active laser as a light source, and echo signals of the water surface and the water bottom are collected according to the radar principle to carry out water depth inversion. The passive optical remote sensing is the most main optical remote sensing water depth inversion mode due to wide observation range and high spatial resolution.
The related scholars paid attention to the water depth remote sensing technology from the 60 th of the 20 th century, and with the successful emission of the remote sensing satellite, a model method for inverting the water depth by using multispectral satellite remote sensing data is rapidly developed, and three forms of a theoretical analytical model, a semi-theoretical semi-empirical model and a statistical model are mainly formed. The theoretical analytical model is based on an underwater light field radiation transmission equation, an analytical expression of the radiance and the water depth and the bottom reflection received by the optical remote sensor is established, and the water depth is calculated through the expression. The semi-theoretical semi-empirical model adopts a method of combining a theoretical model and empirical parameters to realize passive optical remote sensing water depth inversion. The log-linear model is the most widely used semi-theoretical semi-analytical model. The water depth inversion model obtained by directly establishing the statistical relationship between the remote sensing image radiance value and the actually measured water depth value is called a statistical model, and the expression mainly comprises a power function, a logarithmic function and a linear model. The hyperspectral remote sensing has the characteristic of 'map integration', and not only can the spatial information of the ground object be acquired, but also the spectral information of the ground object can be recorded. The hyperspectral remote sensing spectrum information is rich and is a hotspot and a frontier of water depth remote sensing inversion research in recent years. The hyperspectral water depth inversion model mainly comprises a lookup table method, a spectral differential statistical model, a neural network model, a semi-analytical model and the like.
The traditional multispectral and hyperspectral water depth inversion method simulates the radiation attenuation process of sunlight radiation energy through absorption, scattering, reflection and the like of media such as atmosphere, water surface, water body, water bottom and the like, so that the water depth is inverted, the method has strong theoretical basis and high inversion accuracy, but the applicability is limited due to the need of parameter information such as atmosphere, water quality, bottom and the like, and the method cannot be rapidly and effectively popularized and applied to all optical remote sensing loads of the same type under the condition that some parameter information is missing or inaccurate, and further cannot be applied in a large range.
Disclosure of Invention
The invention provides a multispectral remote sensing image water depth inversion method based on chart data, a computer readable storage medium and a terminal device, aiming at solving the technical problem that the conventional multispectral and hyperspectral water depth inversion method cannot be rapidly and effectively popularized to all optical remote sensing loads of the same type under the condition that certain parameter information is missing or inaccurate.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
the multispectral remote sensing image water depth inversion method based on the chart data is characterized by comprising the following steps:
s1, extracting a water body region from a multi-spectrum remote sensing image to obtain the multi-spectrum remote sensing image of the water body region; the multispectral remote sensing image at least comprises four wave bands corresponding to blue light, green light, red light and near infrared light;
s2, performing wave band-by-band sea wave correction on the multispectral remote sensing image of the water body area to obtain a corrected multispectral remote sensing image of the water body area; removing the reef area to obtain a reef-removed multispectral remote sensing image of the water body area;
s3, removing the reef area from the corrected multispectral remote sensing image of the water body area to obtain a reef-removed multispectral remote sensing image of the water body area;
s4, extracting depth information of discrete water depth points in the chart data, and extracting spectral data of corresponding position points in the reef-removed multispectral remote sensing image of the water body area according to 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 obtained in the step S4 and the spectral data of the corresponding position points;
s6, performing pixel-by-pixel water depth inversion on the reef-removed multispectral remote sensing image of the water body region through the wave band ratio water depth inversion model to obtain a preliminary water depth inversion result;
and S7, masking the deep water area in the preliminary water depth inversion result by adopting a threshold value method to obtain water depth image data.
Further, step S0 is further included before step S1, and geometric correction is performed on the multispectral remote sensing image and the chart data.
Further, step S1 specifically includes:
s1.1, calculating a normalized water index NDWI pixel by pixel for the multispectral remote sensing image according to the following formula:
wherein, P Blue Representing blue light band data P in multispectral remote sensing image NIR Representing near infrared light band data in the multispectral remote sensing image;
s1.2, setting an NDWI threshold value of the current water body, and taking the NDWI which corresponds to each pixel in the multispectral remote sensing image and is larger than the NDWI threshold value as a water body area to obtain the multispectral remote sensing image of the water body area.
Further, in step S2, a band-by-band wave correction is performed by the following formula:
P λ1 ＝P λ -k λ (P NIR -min(P NIR ))
wherein, P λ1 Representing the blue, green or red band data, P, in the corrected multispectral remote sensing image λ Representing data in blue, green or red wave bands, P, in a multi-spectral remote sensing image NIR Represents data of near infrared band, min (P) NIR ) Represents the minimum value, k, of the near infrared band data of the current region λ Indicating the correction factor.
Further, the correction coefficient k λ Obtained by the following method:
selecting a deep water area from the multispectral remote sensing image of the water body area, setting a precision step length, traversing all values between 0 and 2 according to the precision step length to obtain a variance corresponding to the deep water area, determining a minimum value of the variance, and taking a value between 0 and 2 corresponding to the minimum value of the variance as a correction coefficient k λ 。
Further, step S3 specifically includes:
s3.1, calculating to obtain a Reef judgment value Reef according to the following formula:
Reef＝P Green -P Blue
wherein, P Green Representing green light wave band data in the multispectral remote sensing image;
s3.2, if the Reef is larger than zero, taking the corresponding position in the multispectral remote sensing image as a Reef area, otherwise, taking the corresponding position as a non-Reef area; and removing the reef area from the corrected multispectral remote sensing image of the water body area to obtain the reef-removed multispectral remote sensing image of the water body area.
Further, step S5 specifically includes:
s5.1, establishing a water depth inversion model with the following wave band ratio:
wherein Depth represents a water Depth value, P Red Representing red light wave band data in the multispectral remote sensing image, wherein a, b and c respectively represent a first parameter, a second parameter and a third parameter of a wave band ratio water depth inversion model;
s5.2, determining a first parameter a, a second parameter b and a third parameter c by using a least square method according to the depth information of a plurality of groups of discrete water depth points and the spectral data of corresponding position points;
and S5.3, obtaining a wave band ratio water depth inversion model.
Meanwhile, the invention also provides a computer readable storage medium, on which a computer program is stored, which is characterized in that the program is executed by a processor to realize the steps of the multispectral remote sensing image water depth inversion method based on the chart data.
In addition, the invention also provides a terminal device, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the processor implements the steps of the multispectral remote sensing image water depth inversion method based on the chart data when executing the computer program.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention relates to a multispectral remote sensing image rapid water depth inversion method based on chart data, which constructs a supervised water depth inversion model, takes easily acquired chart discrete water depth points as supervision data, regresses training model parameters and is applied to a whole scene remote sensing image.
2. According to the method, the water body area can be rapidly and effectively extracted by adopting a water-land separation algorithm based on a normalized water index (NDWI), the influence of the sunlight reflected by the sea waves on the water depth inversion precision can be effectively eliminated by adopting a sea wave removing algorithm based on near-infrared band correction, and the reef area which influences navigation in the water body can be effectively removed by adopting a reef removing method based on a blue-green band ratio method, so that the water depth inversion method is more efficient and accurate.
3. The water depth inversion method provided by the invention can be used for carrying out model parameter regression training of respective regions by aiming at different local regions and utilizing the chart discrete water depth points of the regions, and the water depth inversion precision can reach the optimal local region due to the high uniformity of the parameters of atmosphere, water body and substrate of the local regions. In addition, the water depth inversion method adopts a linear regression model, and is high in operation efficiency and robustness.
4. The invention also provides a computer readable storage medium and terminal equipment capable of executing the steps of the method, and the method can be popularized and applied to realize fusion on corresponding hardware equipment.
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FIG. 1 is a schematic flow chart of an embodiment of a multispectral remote sensing image rapid water depth inversion method based on chart data;
FIG. 2 is a multi-spectral remote sensing image according to an embodiment of the present invention;
FIG. 3 is chart data in an embodiment of the present invention;
FIG. 4 is a graph of band data of various lights in a multi-spectral remote sensing image according to an embodiment of the present invention; wherein, (a) is the wave band data of blue light, (b) is the wave band data of green light, (c) is the wave band data of red light, and (d) is the wave band data of near-infrared light;
FIG. 5 is an NDWI value image in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a water body region extraction result in an embodiment of the invention;
FIG. 7 is a schematic diagram of a wave band-by-band wave correction performed in an embodiment of the present invention;
FIG. 8 is a graph of the water depth values at the water depth points of the actual measurement and inversion of the chart data according to the embodiment of the present invention;
FIG. 9 is a preliminary water depth inversion result obtained in an embodiment of the present invention;
fig. 10 is a schematic diagram of water depth image data finally obtained by masking the deep water region by a threshold method in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
The invention carries out supervised water depth inversion by fusing other easily-obtained multi-source data such as sea chart data, and can promote the large-scale practical application of optical remote sensing water depth inversion. As shown in fig. 1, the water depth inversion method of the present invention includes the following steps:
s1, extracting a water body area from data of the multi-spectrum remote sensing image
Firstly, the invention needs the data of multispectral remote sensing images and chart data, the multispectral remote sensing data input by the invention at least comprises blue, green, red and near infrared bands, geometric correction is needed, each pixel point has latitude and longitude coordinate information, and absolute radiation correction and atmospheric correction are not needed. The input chart data of the corresponding area needs to contain discrete actually-measured depth points and needs to be subjected to geometric correction, namely, each depth point has longitude and latitude coordinate information.
Then, extracting the Water body region from the multispectral remote sensing image data by adopting an NDWI Index method, wherein the NDWI (Normalized Difference Water Index) refers to a Normalized Water Index, and the calculation formula is as follows:
wherein, P Blue Is blue light band data, P, in multispectral remote sensing image NIR The data are near infrared waveband data in the multispectral remote sensing image. And calculating the NDWI pixel by pixel of the multispectral remote sensing image, wherein the area with the NDWI value larger than a threshold Tw is a water body area, and Tw is an empirical parameter and can be set according to the actual condition of the current scene water body and is generally set to be 0.5.
S2, performing wave band-by-band sea wave correction on the water body area extracted in the step S1, and eliminating the influence of the solar energy on the uniformity of the water body radiation
Wave band-by-band wave correction is carried out on a water body area, the influence of the solar shark light on the radiation uniformity of the water body is eliminated, the influence of the waves of other three wave bands of blue, green and red on the solar shark light is corrected by adopting a near-infrared wave band correction method, and the correction formula is as follows:
P λ1 ＝P λ -k λ (P NIR -min(P NIR ))
wherein, P λ1 Representing modified blue, green or red band data, P λ Representing blue, green or red band data, P NIR Represents near infrared band data, min (P) NIR ) And the data is the minimum value of the near infrared band data of the current region. k is a radical of formula λ For the correction coefficients, the blue, green and red band correction coefficients are different. In general k λ The value range is between 0 and 2, the accurate solving method is that a deep water area is selected from the multispectral remote sensing image, the blue, green and red wave band data values of the deep water area after wave correction are reasonably and uniformly distributed, namely the variance is minimum, therefore, the precision step length (such as 0.01) is set, and all values before traversing 0 to 2 ensure that the k with the minimum variance of the deep water area of each wave band is minimum λ I.e. k is the optimum of each band λ 。
S3, removing reef areas influencing navigation in the water body from the multispectral remote sensing image subjected to wave band-by-wave correction in the step S2
Removing reef area in water body affecting navigation, adopting blue-green wave band ratio method, namely considering P Green Values greater than TrXP Blue The area of the value is a Reef area, and the following formula is adopted to judge whether the Reef judgment value Reef is larger than zero or not, namely P can be judged Green Value sum TrxP Blue The magnitude relationship between, tr as an empirical parameter, is typically set to 1.
Reef＝P Green -T r P Blue 。
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
Because the multispectral remote sensing data and the chart data are geometrically corrected, namely the position of each pixel point has longitude and latitude coordinate information, the depth information of the chart discrete water depth point is extracted, and the spectral data of the corresponding position point in the multispectral remote sensing image is extracted according to the longitude and latitude coordinates of the water depth point and is used as the subsequent model training data.
S5, constructing a wave band ratio water depth inversion model
The method comprises the following steps of constructing a wave band ratio water depth inversion model by utilizing the exponential attenuation of sunlight in underwater radiation transmission energy and different wave band attenuation coefficients, wherein the wave band ratio water depth inversion model is as follows:
wherein Depth is a water Depth value, P Blue 、P Green 、P Red The values of blue, green and red wave bands in the multispectral remote sensing image are respectively, a, b and c are parameters, and regression solution can be carried out by using the chart water depth data and the corresponding spectrum data.
S6, solving model parameters by using a least square method
According to the N groups of chart water depth data and the corresponding spectrum data, solving wave band ratio water depth inversion model parameters by using a least square method, wherein the calculation formula is as follows:
θ＝(X T X) -1 X T Y
θ＝[a b c] T
wherein, theta is a coefficient vector to be regressed and solved in the water depth inversion model, X is a multispectral wave band ratio data matrix of N groups of water depth points, Y is water depth data of N groups of water depth points, and P1 Blue Is blue light band data, P2, in a first set of multi-spectral remote sensing images B1ue Is blue light wave band data in a second group of multispectral remote sensing images, and the like, PN Blue Correspondingly, a subscript Green represents Green light wave band data, a subscript Red represents Red light wave band data, a subscript Depth1 represents a first group of water Depth point water Depth data, and the analogy that DepthN represents an Nth group of water Depth point water Depth data.
S7, performing water depth inversion on the multispectral remote sensing image pixel by pixel
And substituting the parameters of the regression solution in the last step into the wave band ratio water depth inversion model to perform pixel-by-pixel water depth inversion on the whole scene multispectral remote sensing image.
And S8, masking the deep water area by a threshold value method to finally obtain water depth image data.
The threshold method is used for masking the deep water area to finally obtain water depth image data, as multispectral remote sensing water depth inversion requires that solar radiation reaches the sea bottom and is reflected out of the water surface, the water depth of the deep water area can not be inverted by an optical remote sensing method in principle, namely, a wave band ratio water depth inversion model is not suitable for the deep water area, a threshold Td masking the deep water area needs to be set, the threshold Td is set according to water quality conditions, and if the Td value of a clear ocean water body is 30 meters, the threshold Td is set according to water quality conditions.
The following is a specific embodiment of the water depth inversion method of the invention:
as shown in fig. 2 and 3, an island region in china is selected, and a water depth inversion experiment is performed by using the water depth inversion method of the present invention to obtain a multispectral remote sensing image and corresponding chart data in the region, wherein the multispectral remote sensing image includes four band data of blue, green, red and near infrared, as shown in fig. 4. Referring to fig. 5 and 6, the water body region is extracted from the multispectral remote sensing image data by using the NDWI index method, and first, the NDWI value of the multispectral remote sensing image of the whole scene is calculated according to the NDWI formula, the threshold Tw is set to 0.5, and when the NDWI value is greater than 0.5, the multispectral remote sensing image is regarded as the water body region. Referring to FIG. 7, the wave band-by-band sea wave correction is performed on the water body region to eliminate the influence of the sunlight on the radiation uniformity of the water body, and the near infrared band correction method is adopted to correct the wave configuration of blue, green and red other three wave bandsAnd the influence of the solar shark light. In the present example, the correction coefficient k λ Corresponding to a value of the blue light corresponding to the correction factor k blue Has an optimal value of 0.5, and a green light corresponding correction coefficient k green Has an optimum value of 0.65 and a correction coefficient k corresponding to red light red The most preferable value of (2) is 0.8. Removing reef areas influencing sailing in the water body by adopting a blue-green wave band ratio method, wherein Tr is 1 in the embodiment, namely P is considered Green Value greater than P Blue The region of values is a reef region, which is excluded when the water depth is inverted. As shown in fig. 8, the depth information of discrete water depth points of the sea chart is extracted, the spectral data of corresponding position points in the multispectral remote sensing image is extracted according to longitude and latitude coordinates of the water depth points, the spectral data is used as the band ratio water depth inversion model training data, and the optimal solution of model parameters a, b and c is obtained through least square regression calculation, in this embodiment, a =232.36, b = -106.33, c = -107.02, the dotted line in fig. 8 represents the water depth value of the actually measured water depth point, and the solid line represents the water depth value of the inverted water depth point. As shown in fig. 9, the wave band ratio water depth inversion model is substituted into the optimal parameters of the local water depth point regression solution, and the pixel-by-pixel water depth inversion is performed on the multispectral remote sensing image of the whole scene to obtain a preliminary water depth inversion result. As shown in fig. 10, the water depth image data is finally obtained by masking the deep water region by a threshold method, where the threshold Td of the deep water region in this embodiment is 20 meters, that is, the water depth of the region can be better inverted by the water depth inversion method of the present invention when the water depth is 0-20 meters.
The water depth inversion method of the present invention can be applied to a computer-readable storage medium having a computer program stored thereon, where the water depth inversion method can be stored as a computer program in the computer-readable storage medium, and the computer program is executed by a processor to implement the steps of the water depth inversion method.
In addition, the water depth inversion method of the present invention can also be applied to a terminal device, where the terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the water depth inversion method of the present invention when executing the computer program. The terminal device here may be a computer, a notebook, a palm computer, and various computing devices such as a cloud server, and the processor may be a general processor, a digital signal processor, an application specific integrated circuit, or other programmable logic devices.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The multispectral remote sensing image water depth inversion method based on the chart data is characterized by comprising the following steps:
s1, extracting a water body region from a multispectral remote sensing image to obtain a multispectral remote sensing image of the water body region; the multispectral remote sensing image at least comprises four wave bands corresponding to blue light, green light, red light and near infrared light;
s2, performing wave band-by-band sea wave correction on the multispectral remote sensing image of the water body area to obtain a corrected multispectral remote sensing image of the water body area;
s3, removing the reef area from the corrected multispectral remote sensing image of the water body area to obtain a reef-removed multispectral remote sensing image of the water body area;
s4, extracting depth information of discrete water depth points in the chart data, and extracting spectral data of corresponding position points in the reef-removed multispectral remote sensing image of the water body area according to 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 obtained in the step S4 and the spectral data of the corresponding position points;
s6, performing pixel-by-pixel water depth inversion on the reef-removed multispectral remote sensing image of the water body region through the wave band ratio water depth inversion model to obtain a preliminary water depth inversion result;
and S7, masking the deep water area in the preliminary water depth inversion result by adopting a threshold value method to obtain water depth image data.
2. The method for inverting the water depth of the multispectral remote sensing image based on the sea map data as claimed in claim 1, wherein the method comprises the following steps: step S0 is also included before step S1, and geometric correction is carried out on the multispectral remote sensing image and the chart data.
3. The method for inverting the water depth of the multispectral remote sensing image based on the sea map data according to claim 1 or 2, wherein the step S1 specifically comprises the following steps:
s1.1, calculating a normalized water index NDWI pixel by pixel for the multispectral remote sensing image according to the following formula:
wherein, P Blue Data representing blue light bands, P, in a multi-spectral remote sensing image NIR Representing near infrared light band data in the multispectral remote sensing image;
s1.2, setting an NDWI threshold value of the current water body, and taking the NDWI which corresponds to each pixel in the multispectral remote sensing image and is larger than the NDWI threshold value as a water body area to obtain the multispectral remote sensing image of the water body area.
4. The method for inverting the water depth of the multispectral remote sensing image based on the sea map data as recited in claim 3, wherein in the step S2, the wave band-by-wave band sea wave correction is performed according to the following formula:
wherein,
representing blue, green or red band data, P, in the corrected multispectral remote sensing image λ Representing data in blue, green or red wave bands, P, in a multi-spectral remote sensing image NIR Represents data of near infrared band, min (P) NIR ) Indicating that the current region is closeMinimum value of infrared band data, k λ Indicating the correction factor.
5. The method for inverting the water depth of the multispectral remote sensing image based on the sea chart data as claimed in claim 4, wherein the correction coefficient k is λ Obtained by the following method:
selecting a deep water area from the multispectral remote sensing image of the water body area, setting a precision step length, traversing all values between 0 and 2 according to the precision step length to obtain a variance corresponding to the deep water area, determining a minimum value of the variance, and taking the value between 0 and 2 corresponding to the minimum value of the variance as a correction coefficient k λ 。
6. The method for inverting the water depth of the multispectral remote sensing image based on the sea map data according to claim 5, wherein the step S3 specifically comprises the following steps:
s.3.1, calculating a Reef judgment value Reef according to the following formula:
Reef＝P Green -P Blue
wherein, P Green Representing green light wave band data in the multispectral remote sensing image;
s3.2, if the Reef is larger than zero, taking the corresponding position in the multispectral remote sensing image as a Reef area, otherwise, taking the corresponding position as a non-Reef area; and removing the reef area from the corrected multispectral remote sensing image of the water body area to obtain the reef-removed multispectral remote sensing image of the water body area.
7. The method for inverting the water depth of the multispectral remote sensing image based on the chart data as claimed in claim 6, wherein the step S5 is specifically as follows:
s5.1, establishing a water depth inversion model with the following wave band ratio:
wherein Depth represents a Depth value, P Red Representing red light wave band data in multispectral remote sensing image, a, b and c respectively represent waveA first parameter, a second parameter and a third parameter of the section ratio water depth inversion model;
s5.2, determining a first parameter a, a second parameter b and a third parameter c by using a least square method according to the depth information of a plurality of groups of discrete water depth points and the spectral data of corresponding position points;
and S5.3, obtaining a wave band ratio water depth inversion model.
8. A computer-readable storage medium having stored thereon a computer program, characterized in that: the program is executed by a processor to realize the steps of the multispectral remote sensing image water depth inversion method based on the sea chart data according to any one of claims 1 to 7.
9. 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, when executing the computer program, implements the steps of the method for water depth inversion based on multispectral remote sensing image of any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116975497A (en) * | 2023-08-07 | 2023-10-31 | 河北地质大学 | Normalized water quality index calculation method based on difference |
| CN117237430A (en) * | 2023-11-10 | 2023-12-15 | 中国地质大学（武汉） | High-precision multi-time-sequence water depth inversion method, computing equipment and storage medium |
| CN117274831A (en) * | 2023-09-04 | 2023-12-22 | 大连海事大学 | Offshore turbid water body depth inversion method based on machine learning and hyperspectral satellite remote sensing image |
| CN117975255A (en) * | 2024-04-02 | 2024-05-03 | 国家海洋信息中心 | Shallow sea bottom type identification method for multispectral remote sensing image |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116975497A (en) * | 2023-08-07 | 2023-10-31 | 河北地质大学 | Normalized water quality index calculation method based on difference |
| CN117274831A (en) * | 2023-09-04 | 2023-12-22 | 大连海事大学 | Offshore turbid water body depth inversion method based on machine learning and hyperspectral satellite remote sensing image |
| CN117237430A (en) * | 2023-11-10 | 2023-12-15 | 中国地质大学（武汉） | High-precision multi-time-sequence water depth inversion method, computing equipment and storage medium |
| CN117237430B (en) * | 2023-11-10 | 2024-03-08 | 中国地质大学（武汉） | High-precision multi-time-sequence water depth inversion method, computing equipment and storage medium |
| CN117975255A (en) * | 2024-04-02 | 2024-05-03 | 国家海洋信息中心 | Shallow sea bottom type identification method for multispectral remote sensing image |
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