CN117969363A - SDGSAT-1 satellite-based suspended matter concentration inversion method and system - Google Patents
SDGSAT-1 satellite-based suspended matter concentration inversion method and system Download PDFInfo
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Abstract
The invention provides a method and a system for inverting suspended matter concentration based on SDGSAT-1 satellites. The method comprises the following steps: performing radiation calibration on SDGSAT-1 images of the region of interest; performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into earth surface reflectivity; calculating a normalized water index according to the earth surface reflectivity; carrying out bimodal threshold segmentation on the normalized water body index by combining with an OTSU algorithm to extract a water body; calculating a classification index according to the earth surface reflectivity of the extracted water body image, and setting an index threshold; according to the classification index and the index threshold, applying the surface reflectivity of the extracted water body image to calculate the suspended matter concentration index; and calculating the suspended matter concentration by using the suspended matter concentration index. The scheme provided by the invention can improve the phenomenon that the concentration of the inversion suspended matter of the common semi-analytic algorithm is invalid on the severe turbid water body, and has higher applicability to the water bodies with different turbidity degrees.
Description
Technical Field
The invention belongs to the field of water quality monitoring, and particularly relates to a SDGSAT-1 satellite-based suspended matter concentration inversion method and system.
Background
The total suspended matters (Total Suspended Matter concentration, TSM) in the water body are the general names of organic suspended matters and inorganic suspended matters and mainly comprise plankton, animal and plant remains, non-pigment cell matters of the plankton, suspended sediment and the like. The suspended matters can directly influence the propagation process of light in a water body, the ecological function of the water body and element geochemical circulation in the aquatic ecological system, and can influence the redistribution process and vertical distribution of underwater light energy, determine the transparency, the depth of a true light layer, the water color and other optical properties of the water body, and have important influence on photosynthesis and primary productivity level of underwater phytoplankton. Therefore, the inversion of the suspended matter concentration has important significance for deeply understanding the dynamic change process of the water body and accurately evaluating the primary productivity of the water body.
The conventional water quality monitoring has slow investigation speed and long monitoring period, and is difficult to meet the requirement on large-area water quality monitoring. The remote sensing technology is used as a large-scale water environment investigation and monitoring means, and can overcome the defects of the conventional water quality monitoring method. The suspension inversion algorithm based on the QAA model is a semi-analytical algorithm which is most commonly used for inversion of the suspension concentration at present, but the suspension concentration calculated by the method has larger deviation on a severely turbid water body.
Disclosure of Invention
In order to solve the technical problems, the invention provides a technical scheme of a SDGSAT-1 satellite-based suspended matter concentration inversion method, so as to solve the technical problems.
The invention discloses a method for inverting the concentration of a suspension based on SDGSAT-1 satellites, which comprises the following steps:
s1, downloading SDGSAT-1 images on an SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
S2, performing radiation calibration on SDGSAT-1 images of the region of interest, namely converting original DN values recorded by SDGSAT-1 images into radiation brightness values;
s3, performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into earth surface reflectivity;
S4, calculating a normalized water index according to the surface reflectivity; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
S5, calculating a classification index according to the earth surface reflectivity of the extracted water body image, and setting an index threshold;
S6, according to the classification index and the index threshold, applying the surface reflectivity of the extracted water body image to calculate the suspended matter concentration index; and calculating the suspended matter concentration by using the suspended matter concentration index.
According to the method of the first aspect of the present invention, in the step S5, the method for calculating the classification index according to the surface reflectance of the image after extracting the water body includes:
And calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image.
According to the method of the first aspect of the present invention, in the step S5, the method for calculating the classification index according to the surface reflectivities of the green band, the red band and the blue band of the image after extracting the water body includes:
Wherein, B3 is the surface reflectivity of the third wave band of SDGSAT-1 image, namely the surface reflectivity of the blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
According to the method of the first aspect of the invention, in said step S5, said index threshold is equal to 0.56.
According to the method of the first aspect of the present invention, in the step S6, the method for calculating the suspended matter concentration index by applying the surface reflectivity of the image after extracting the water body according to the classification index and the index threshold value includes:
And according to the classification index and the index threshold, applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the image after extracting the water body to calculate the suspended matter concentration index.
According to the method of the first aspect of the present invention, in the step S6, the method for calculating the suspended matter concentration index by applying the surface reflectivities of the green band, the red band and the blue band of the image after extracting the water body according to the classification index and the index threshold value includes:
If the classification indicator is < an indicator threshold,
If the classification index is more than or equal to the index threshold value,
Wherein TI is a suspended matter concentration index; b3 is the surface reflectivity of a third wave band of SDGSAT-1 images, namely the surface reflectivity of a blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
According to the method of the first aspect of the present invention, in the step S6, the method for calculating the suspended matter concentration using the suspended matter concentration index includes:
TSM＝10TI
Wherein TI is a suspended matter concentration index; TSM is the suspension concentration.
In a second aspect, the invention discloses a SDGSAT-1 satellite-based suspension concentration inversion system, the system comprising:
a first processing module configured to download SDGSAT-1 images at the SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
A second processing module configured to perform radiometric calibration on the SDGSAT-1 image of the region of interest, i.e., converting the original DN value recorded on the SDGSAT-1 image into a radiance value;
a third processing module configured to perform atmospheric correction on the radiance value, converting the radiance value into a surface reflectance;
A fourth processing module configured to calculate a normalized water index from the surface reflectance; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
a fifth processing module configured to calculate a classification index according to the surface reflectance of the image after extracting the water body, and set an index threshold;
A sixth processing module configured to calculate a suspended matter concentration index by applying a surface reflectance of the image after extraction of the water body according to the classification index and the index threshold; and calculating the suspended matter concentration by using the suspended matter concentration index.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory storing a computer program, the processor implementing the steps in a SDGSAT-1 satellite-based suspension concentration inversion method of any one of the first aspects of the present disclosure when the computer program is executed.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a SDGSAT-1 satellite-based suspension concentration inversion method of any of the first aspects of the present disclosure.
In conclusion, the scheme provided by the invention can improve the phenomenon that the concentration of the inversion suspended matters of the common semi-analytical algorithm is invalid on the severely turbid water body, and has higher applicability to the water bodies with different turbidity degrees.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for performing SDGSAT-1 satellite-based suspension concentration inversion according to an embodiment of the present invention;
FIG. 2 is a block diagram of a SDGSAT-1 satellite-based suspended solids concentration inversion system according to an embodiment of the invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention discloses a SDGSAT-1 satellite-based suspension concentration inversion method. FIG. 1 is a flow chart of a method for performing SDGSAT-1 satellite-based suspension concentration inversion according to an embodiment of the present invention, as shown in FIG. 1, the method includes:
s1, downloading SDGSAT-1 images on an SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
S2, performing radiation calibration on SDGSAT-1 images of the region of interest, namely converting original DN values recorded by SDGSAT-1 images into radiation brightness values;
s3, performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into earth surface reflectivity;
S4, calculating a normalized water index according to the surface reflectivity; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
S5, calculating a classification index according to the earth surface reflectivity of the extracted water body image, and setting an index threshold;
S6, according to the classification index and the index threshold, applying the surface reflectivity of the extracted water body image to calculate the suspended matter concentration index; and calculating the suspended matter concentration by using the suspended matter concentration index.
In step S1, downloading SDGSAT-1 images on the SDG big data platform; the SDGSAT-1 image is cropped to the region of interest in ENVI.
Specifically, the MII multispectral image contains 7 bands, as shown in table 1. The 3 rd wave band is positioned in the blue light range, the 4 th wave band is positioned in the green light range, the 5 th wave band is positioned in the red light wave band, and the wave band combination of 5, 4 and 3 is the wave band combination of SDGSAT-1 multispectral image RGB true color synthesis.
TABLE 1
In step S2, the SDGSAT-1 image of the region of interest is radiometric scaled, i.e., the raw DN values recorded by the SDGSAT-1 image are converted into radiance values.
Specifically, according to gain coefficient gain and Bias of each wave band given by the image calib.xml file, the radiance L is calculated, and the calculation formula is as follows:
L＝gain*DN+Bias。
and in step S3, performing atmospheric correction on the radiance value, and converting the radiance value into the surface reflectivity.
Specifically, acolite (https:// odnature. Natural sciences. Be/remsem/software-and-data/acolite) is a versatile atmospheric correction processor provided by RBINS (Royal Belgian Institute of Natural Sciences) that can be used for water applications for a variety of satellite tasks. Acolite can simply and quickly process images from various satellites, including Landsat (5/7/8/9), sentinel-2/MSI (A/B), sentinel-3/OLCI (A/B), quickBird2, worldView-2, etc., as well as several hyperspectral sensors (CHRIS, HYPERION, HICO, PRISMA and DESIS) for coastal and inland water applications. In recent years, acolite is also supplemented with an atmosphere correction module for a domestic satellite SDGSAT-1 multispectral sensor (MII), and the possibility is provided for carrying out atmosphere correction on SDGSAT-1 images by adopting Acolite. In this embodiment, acolite modules are used to perform atmospheric correction on SDGSAT-1 satellite images.
The Acolite processor uses a dark spectrum fitting algorithm (Dark Spectrum Fitting, hereinafter DSF) to make atmospheric corrections (Quinten Vanhellemont et al., 2018). It is well known that in turbid or high-productivity bodies of water, the reflectivity of the near infrared band is not negligible, so DSF algorithms do not assume that any one band has negligible reflectivity of the body of water, but instead look from the image for the best "dark" target based on the lowest zenith reflectivity, i.e. the band where the least range of radiation can be estimated, even possibly using non-aqueous targets for the estimation of atmospheric range radiation. The algorithm utilizes more ground information, so that the algorithm can be better applied to a sensor with meter-level spatial resolution to improve the accuracy of an atmospheric correction result. Research has shown that DSF algorithms are particularly well suited for atmospheric correction of turbid water bodies, as well as clean water bodies and land (Quinten Vanhellemont et al., 2021).
The packaged Acolite module, which contains sdgsat sub-modules for processing SDGSAT-1 multispectral images, is downloaded over Github (https:// gitsub.com/acolite/acolite), the module being written in the python language. The python main function is written, the Acolite module is called, and the atmospheric correction is run. The program input is a folder composed of images of the multiple views SDGSAT-1, and the program input is a folder composed of satellite images with corresponding numbers and Acolite subjected to atmosphere correction.
Step S4, calculating a normalized water index according to the surface reflectivity; and carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm, and extracting the water body.
In particular, since the river distribution is broken, especially for a river having a serpentine and complex shape, the influence of the adjacent pixels on the land is large. Therefore, the accuracy of water range extraction directly affects the accuracy and effect of water inversion. Moreover, the water quality research requirement of large area scale and long time sequence can be met only by realizing automatic extraction.
MCFEETERS in 1996, proposed a normalized water index NDWI, which is a water information extraction method constructed by using a band difference ratio. The water body has strong absorptivity to near infrared, the reflectivity of land and vegetation in near infrared wave bands is obviously enhanced, the spectrum is selected in the green light and near infrared channel range, and the difference between the water body and other land features is highlighted (MCFEETERS, 1996). The NDWI calculation formula is as follows:
Wherein NDWI is a normalized water index; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b7 is the surface reflectance of the seventh band of SDGSAT-1 images, i.e. the surface reflectance of the near infrared band.
The water index has a good inhibition effect on interference factors around the water body, so the index is used for extracting the water body. In general, extracting a water body based on a water body index is to calculate an optimal threshold or manually select an empirical threshold to distinguish the water body and non-water body parts of each remote sensing image. The invention expands outwards by 1.5 times the area according to the general vector boundary of the water body, and the threshold value is determined in the range. For a large amount of long time sequence remote sensing data, a large amount of time is required to be consumed for manually determining the threshold value, and the water body extraction precision of each image cannot be optimized by adopting a unified experience threshold value. Therefore, the embodiment combines the OTSU algorithm to perform bimodal threshold segmentation on the NDWI, realizes automatic determination of the water threshold of the scene-by-scene image, and more efficiently, accurately and automatically distinguishes the water and non-water parts of each scene remote sensing image. The OTSU algorithm is also called maximum inter-class variance method and is derived based on the least squares principle. The basic idea is to calculate the occurrence probability of each gray level based on the histogram of the image, divide all pixels constituting the image into two classes with a certain threshold variable t, then calculate the inter-class variance of each class, and select the t value when the variance between the two classes is the largest as the optimal threshold of the binarization processing. The algorithm can adaptively determine the optimal threshold for each image based on the histograms of the different images (Huang Ancai, 2019). Therefore, the NDWI combined with the OTSU algorithm can effectively realize large-scale and long-time-sequence water body extraction, and compared with a global unified threshold, the accuracy of water body extraction can be improved to a certain extent.
In step S5, a classification index is calculated according to the earth surface reflectivity of the extracted water body image, and an index threshold is set.
In some embodiments, in the step S5, the method for calculating the classification index according to the surface reflectivity of the image after extracting the water body includes:
And calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image.
The method for calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image comprises the following steps:
Wherein, B3 is the surface reflectivity of the third wave band of SDGSAT-1 image, namely the surface reflectivity of the blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of a fifth wave band of SDGSAT-1 images, namely the surface reflectivity of a red wave band;
The index threshold is equal to 0.56.
In step S6, according to the classification index and the index threshold, the surface reflectivity of the image after extracting the water body is applied, and the suspended matter concentration index is calculated; and calculating the suspended matter concentration by using the suspended matter concentration index.
In some embodiments, in the step S6, the method for calculating the suspended matter concentration index according to the classification index and the index threshold value by applying the surface reflectivity of the image after extracting the water body includes:
And according to the classification index and the index threshold, applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the image after extracting the water body to calculate the suspended matter concentration index.
The method for calculating the suspended matter concentration index according to the classification index and the index threshold value by applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image comprises the following steps:
If the classification indicator is < an indicator threshold,
If the classification index is more than or equal to the index threshold value,
Wherein TI is a suspended matter concentration index; b3 is the surface reflectivity of a third wave band of SDGSAT-1 images, namely the surface reflectivity of a blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
The method for calculating the suspended matter concentration by using the suspended matter concentration index comprises the following steps:
TSM＝10TI
wherein TI is a suspended matter concentration index; TSM is the suspension concentration TSM (mg/L).
In conclusion, the scheme provided by the invention can improve the phenomenon that the concentration of the inversion suspended matters is invalid on the severely turbid water body by a common semi-analytic algorithm, and has higher applicability to water bodies with different turbidity degrees; the method has higher precision in the similar atmospheric correction method, and can be better applied to the sensor with meter-level spatial resolution, thereby improving the precision and reliability of large-scale long-time sequence water quality monitoring by utilizing high-resolution satellite data.
In a second aspect, the invention discloses a SDGSAT-1 satellite-based suspended matter concentration inversion system. FIG. 2 is a block diagram of a SDGSAT-1 satellite-based suspended solids concentration inversion system according to an embodiment of the invention; as shown in fig. 2, the system 100 includes:
A first processing module 101 configured to download SDGSAT-1 images at the SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
A second processing module 102 configured to perform radiometric calibration on the SDGSAT-1 image of the region of interest, i.e., converting the raw DN values recorded by the SDGSAT-1 image into radiance values;
A third processing module 103 configured to perform atmospheric correction on the radiance value, converting the radiance value into a surface reflectance;
A fourth processing module 104 configured to calculate a normalized water index from the surface reflectance; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
A fifth processing module 105 configured to calculate a classification index according to the surface reflectance of the image after extracting the water body, and set an index threshold;
A sixth processing module 106 configured to calculate a suspended matter concentration index by applying a surface reflectance of the extracted image of the water body according to the classification index and the index threshold; and calculating the suspended matter concentration by using the suspended matter concentration index.
According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to include 7 bands in the MII multispectral image, as shown in table 1. The 3 rd wave band is positioned in the blue light range, the 4 th wave band is positioned in the green light range, the 5 th wave band is positioned in the red light wave band, and the wave band combination of 5, 4 and 3 is the wave band combination of SDGSAT-1 multispectral image RGB true color synthesis.
TABLE 1
According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to calculate the radiance L according to the gain coefficient gain and the Bias of each band given by the image calib.
L＝gain*DN+Bias。
The system according to the second aspect of the present invention, the third processing module 103 is specifically configured such that Acolite (https:// odnature. Natural sources/be/remsem/software-and-data/acolite) is a general purpose atmospheric correction processor provided by RBINS (Royal Belgian Institute of Natural Sciences), which can be used for water applications for a variety of satellite tasks. Acolite can simply and quickly process images from various satellites, including Landsat (5/7/8/9), sentinel-2/MSI (A/B), sentinel-3/OLCI (A/B), quickBird2, worldView-2, etc., as well as several hyperspectral sensors (CHRIS, HYPERION, HICO, PRISMA and DESIS) for coastal and inland water applications. In recent years, acolite is also supplemented with an atmosphere correction module for a domestic satellite SDGSAT-1 multispectral sensor (MII), and the possibility is provided for carrying out atmosphere correction on SDGSAT-1 images by adopting Acolite. In this embodiment, acolite modules are used to perform atmospheric correction on SDGSAT-1 satellite images.
The Acolite processor uses a dark spectrum fitting algorithm (Dark Spectrum Fitting, hereinafter DSF) to make atmospheric corrections (Quinten Vanhellemont et al., 2018). It is well known that in turbid or high-productivity bodies of water, the reflectivity of the near infrared band is not negligible, so DSF algorithms do not assume that any one band has negligible reflectivity of the body of water, but instead look from the image for the best "dark" target based on the lowest zenith reflectivity, i.e. the band where the least range of radiation can be estimated, even possibly using non-aqueous targets for the estimation of atmospheric range radiation. The algorithm utilizes more ground information, so that the algorithm can be better applied to a sensor with meter-level spatial resolution to improve the accuracy of an atmospheric correction result. Research has shown that DSF algorithms are particularly well suited for atmospheric correction of turbid water bodies, as well as clean water bodies and land (Quinten Vanhellemont et al., 2021).
The packaged Acolite module, which contains sdgsat sub-modules for processing SDGSAT-1 multispectral images, is downloaded over Github (https:// gitsub.com/acolite/acolite), the module being written in the python language. The python main function is written, the Acolite module is called, and the atmospheric correction is run. The program input is a folder composed of images of the multiple views SDGSAT-1, and the program input is a folder composed of satellite images with corresponding numbers and Acolite subjected to atmosphere correction.
According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured to be greatly affected by the land-adjacent pixels, since the river distribution is relatively broken, especially for a river with a serpentine shape. Therefore, the accuracy of water range extraction directly affects the accuracy and effect of water inversion. Moreover, the water quality research requirement of large area scale and long time sequence can be met only by realizing automatic extraction.
MCFEETERS in 1996, proposed a normalized water index NDWI, which is a water information extraction method constructed by using a band difference ratio. The water body has strong absorptivity to near infrared, the reflectivity of land and vegetation in near infrared wave bands is obviously enhanced, the spectrum is selected in the green light and near infrared channel range, and the difference between the water body and other land features is highlighted (MCFEETERS, 1996). The NDWI calculation formula is as follows:
Wherein NDWI is a normalized water index; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b7 is the surface reflectance of the seventh band of SDGSAT-1 images, i.e. the surface reflectance of the near infrared band.
The water index has a good inhibition effect on interference factors around the water body, so the index is used for extracting the water body. In general, extracting a water body based on a water body index is to calculate an optimal threshold or manually select an empirical threshold to distinguish the water body and non-water body parts of each remote sensing image. The invention expands outwards by 1.5 times the area according to the general vector boundary of the water body, and the threshold value is determined in the range. For a large amount of long time sequence remote sensing data, a large amount of time is required to be consumed for manually determining the threshold value, and the water body extraction precision of each image cannot be optimized by adopting a unified experience threshold value. Therefore, the embodiment combines the OTSU algorithm to perform bimodal threshold segmentation on the NDWI, realizes automatic determination of the water threshold of the scene-by-scene image, and more efficiently, accurately and automatically distinguishes the water and non-water parts of each scene remote sensing image. The OTSU algorithm is also called maximum inter-class variance method and is derived based on the least squares principle. The basic idea is to calculate the occurrence probability of each gray level based on the histogram of the image, divide all pixels constituting the image into two classes with a certain threshold variable t, then calculate the inter-class variance of each class, and select the t value when the variance between the two classes is the largest as the optimal threshold of the binarization processing. The algorithm can adaptively determine the optimal threshold for each image based on the histograms of the different images (Huang Ancai, 2019). Therefore, the NDWI combined with the OTSU algorithm can effectively realize large-scale and long-time-sequence water body extraction, and compared with a global unified threshold, the accuracy of water body extraction can be improved to a certain extent.
According to the system of the second aspect of the present invention, the fifth processing module 105 is specifically configured to calculate the classification index according to the surface reflectivity of the image after extracting the water body, where the method includes:
And calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image.
The method for calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image comprises the following steps:
Wherein, B3 is the surface reflectivity of the third wave band of SDGSAT-1 image, namely the surface reflectivity of the blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of a fifth wave band of SDGSAT-1 images, namely the surface reflectivity of a red wave band;
The index threshold is equal to 0.56.
According to the system of the second aspect of the present invention, the sixth processing module 106 is specifically configured to, according to the classification index and the index threshold, apply the surface reflectivity of the image after extracting the water body, and the method for calculating the suspended matter concentration index includes:
And according to the classification index and the index threshold, applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the image after extracting the water body to calculate the suspended matter concentration index.
The method for calculating the suspended matter concentration index according to the classification index and the index threshold value by applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image comprises the following steps:
If the classification indicator is < an indicator threshold,
If the classification index is more than or equal to the index threshold value,
Wherein TI is a suspended matter concentration index; b3 is the surface reflectivity of a third wave band of SDGSAT-1 images, namely the surface reflectivity of a blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
The method for calculating the suspended matter concentration by using the suspended matter concentration index comprises the following steps:
TSM＝10TI
wherein TI is a suspended matter concentration index; TSM is the suspension concentration TSM (mg/L).
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory storing a computer program, the processor implementing the steps in a SDGSAT-1 satellite-based suspension concentration inversion method according to any one of the first aspects of the present disclosure when the computer program is executed.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the technical solution of the present disclosure is applied, and that a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a SDGSAT-1 satellite-based suspension concentration inversion method according to any one of the first aspects of the present disclosure.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for performing a SDGSAT-1 satellite-based suspension concentration inversion, the method comprising:
s1, downloading SDGSAT-1 images on an SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
S2, performing radiation calibration on SDGSAT-1 images of the region of interest, namely converting original DN values recorded by SDGSAT-1 images into radiation brightness values;
s3, performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into earth surface reflectivity;
S4, calculating a normalized water index according to the surface reflectivity; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
S5, calculating a classification index according to the earth surface reflectivity of the extracted water body image, and setting an index threshold;
S6, according to the classification index and the index threshold, applying the surface reflectivity of the extracted water body image to calculate the suspended matter concentration index; and calculating the suspended matter concentration by using the suspended matter concentration index.
2. The method for performing a SDGSAT-1 satellite-based inversion of suspended matter concentration according to claim 1, wherein in said step S5, said method for calculating a classification index from the surface reflectivity of the extracted water body image comprises:
And calculating the classification index according to the surface reflectivities of the green wave band, the red wave band and the blue wave band of the extracted water body image.
3. The method for performing the inversion of the suspended matter concentration based on SDGSAT-1 satellites according to claim 2, wherein in the step S5, the method for calculating the classification index according to the surface reflectivities of the green band, the red band and the blue band of the image after extracting the water body comprises:
Wherein, B3 is the surface reflectivity of the third wave band of SDGSAT-1 image, namely the surface reflectivity of the blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
4. A method of performing a SDGSAT-1 satellite based suspension concentration inversion according to claim 1, wherein in step S5 the index threshold is equal to 0.56.
5. The method for performing a SDGSAT-1 satellite-based suspension concentration inversion according to claim 1, wherein in the step S6, the method for calculating a suspension concentration index by applying the surface reflectivity of the extracted image of the water body according to the classification index and the index threshold value comprises:
And according to the classification index and the index threshold, applying the surface reflectivities of the green wave band, the red wave band and the blue wave band of the image after extracting the water body to calculate the suspended matter concentration index.
6. The method for performing a SDGSAT-1 satellite-based suspension concentration inversion according to claim 5, wherein in the step S6, the method for calculating the suspension concentration index by applying the surface reflectivities of the green band, the red band and the blue band of the extracted image of the water body according to the classification index and the index threshold comprises:
If the classification indicator is < an indicator threshold,
If the classification index is more than or equal to the index threshold value,
Wherein TI is a suspended matter concentration index; b3 is the surface reflectivity of a third wave band of SDGSAT-1 images, namely the surface reflectivity of a blue wave band; b4 is the surface reflectivity of a fourth wave band of SDGSAT-1 images, namely the surface reflectivity of a green wave band; b5 is the surface reflectivity of the fifth wave band of SDGSAT-1 images, namely the surface reflectivity of the red wave band.
7. A method of inverting the concentration of suspended solids based on SDGSAT-1 satellite of claim 1, wherein in said step S6, said method of calculating the concentration of suspended solids using said concentration of suspended solids index comprises:
TSM＝10TI
Wherein TI is a suspended matter concentration index; TSM is the suspension concentration.
8. A suspension concentration inversion system for SDGSAT-1 satellite-based, the system comprising:
a first processing module configured to download SDGSAT-1 images at the SDG big data platform; clipping the SDGSAT-1 images to a region of interest in ENVI;
A second processing module configured to perform radiometric calibration on the SDGSAT-1 image of the region of interest, i.e., converting the original DN value recorded on the SDGSAT-1 image into a radiance value;
a third processing module configured to perform atmospheric correction on the radiance value, converting the radiance value into a surface reflectance;
A fourth processing module configured to calculate a normalized water index from the surface reflectance; carrying out bimodal threshold segmentation on the normalized water body index by combining an OTSU algorithm to extract a water body;
a fifth processing module configured to calculate a classification index according to the surface reflectance of the image after extracting the water body, and set an index threshold;
A sixth processing module configured to calculate a suspended matter concentration index by applying a surface reflectance of the image after extraction of the water body according to the classification index and the index threshold; and calculating the suspended matter concentration by using the suspended matter concentration index.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a SDGSAT-1 satellite-based suspension concentration inversion method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of a SDGSAT-1 satellite-based suspension concentration inversion method according to any one of claims 1 to 7.
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