CN117969363A - SDGSAT-1 satellite-based suspended matter concentration inversion method and system - Google Patents
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Abstract
本发明提出一种基于SDGSAT‑1卫星的悬浮物浓度反演方法和系统。其中，方法包括：对感兴趣区域的SDGSAT‑1图像进行辐射定标；对辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率；根据地表反射率计算归一化水体指数；结合OTSU算法对归一化水体指数进行双峰阈值分割，提取水体；根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值；根据分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。本发明提出的方案能够对常见的半解析算法在在重度浑浊水体上反演悬浮物浓度失效的现象进行了改进，对于不同浑浊程度的水体具有较高的适用性。
The present invention proposes a method and system for inverting suspended matter concentration based on the SDGSAT-1 satellite. The method includes: performing radiation calibration on the SDGSAT-1 image of the area of interest; performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into surface reflectivity; calculating the normalized water index according to the surface reflectivity; performing bimodal threshold segmentation on the normalized water index in combination with the OTSU algorithm to extract the water body; calculating the classification index according to the surface reflectivity of the image after the water body is extracted, and setting the index threshold; according to the classification index and the index threshold, applying the surface reflectivity of the image after the water body is extracted, calculating the suspended matter concentration index; applying the suspended matter concentration index to calculate the suspended matter concentration. The scheme proposed by the present invention can improve the phenomenon that the common semi-analytical algorithm fails to invert the suspended matter concentration in severely turbid water bodies, and has high applicability to water bodies with different turbidity levels.
Description
技术领域Technical Field
本发明属于水质监测领域，尤其涉及一种基于SDGSAT-1卫星的悬浮物浓度反演方法和系统。The invention belongs to the field of water quality monitoring, and in particular relates to a suspended matter concentration inversion method and system based on the SDGSAT-1 satellite.
背景技术Background technique
水体中的总悬浮物(Total Suspended Matter concentration,TSM)是有机悬浮物和无机悬浮物的总称，主要包括浮游生物、动植物遗体、浮游植物非色素细胞物质和悬浮物泥沙等。悬浮物在水生生态系统中会直接影响光在水体中的传播过程及水体的生态功能和元素地球化学循环，且会影响水下光能的再分配过程和垂向分布，决定了水体的透明度、真光层深度、水色等光学性质，对水下浮游植物的光合作用和初级生产力水平产生了重要影响。因此，悬浮物浓度的反演对于深入理解水体动力变化过程、精确评价水体初级生产力具有重要意义。Total suspended matter (TSM) in water bodies is a general term for organic suspended matter and inorganic suspended matter, mainly including plankton, animal and plant remains, phytoplankton non-pigmented cell substances and suspended sediment. In aquatic ecosystems, suspended matter directly affects the propagation of light in water bodies, the ecological functions of water bodies and the geochemical cycle of elements, and affects the redistribution process and vertical distribution of underwater light energy, determining the transparency, true light layer depth, water color and other optical properties of water bodies, and has an important impact on the photosynthesis and primary productivity level of underwater phytoplankton. Therefore, the inversion of suspended matter concentration is of great significance for a deep understanding of the dynamic changes of water bodies and accurate evaluation of the primary productivity of water bodies.
常规的水质监测调查速度慢、监测周期长，难以满足对大面积水质监测的要求。遥感技术作为一种大范围水环境调查和监测手段，可以克服常规水质监测方法的不足。基于QAA模型的悬浮物反演算法是目前反演悬浮物浓度最常用的一种半解析算法，但该方法在重度浑浊水体上所计算的悬浮物浓度存在较大的偏差。Conventional water quality monitoring surveys are slow and have long monitoring cycles, making it difficult to meet the requirements for large-scale water quality monitoring. Remote sensing technology, as a means of large-scale water environment survey and monitoring, can overcome the shortcomings of conventional water quality monitoring methods. The suspended matter inversion algorithm based on the QAA model is currently the most commonly used semi-analytical algorithm for inverting suspended matter concentration, but this method has a large deviation in the suspended matter concentration calculated in severely turbid water bodies.
发明内容Summary of the invention
为解决上述技术问题，本发明提出一种基于SDGSAT-1卫星的悬浮物浓度反演方法的技术方案，以解决上述技术问题。In order to solve the above technical problems, the present invention proposes a technical solution of a suspended matter concentration inversion method based on the SDGSAT-1 satellite to solve the above technical problems.
本发明第一方面公开了一种基于SDGSAT-1卫星的悬浮物浓度反演方法，所述方法包括：The first aspect of the present invention discloses a suspended matter concentration inversion method based on the SDGSAT-1 satellite, the method comprising:
步骤S1、在SDG大数据平台下载SDGSAT-1图像；在ENVI中将所述SDGSAT-1图像裁剪至感兴趣区域；Step S1, downloading SDGSAT-1 images on the SDG big data platform; cropping the SDGSAT-1 images to the region of interest in ENVI;
步骤S2、对感兴趣区域的SDGSAT-1图像进行辐射定标，即将SDGSAT-1图像记录的原始DN值转换为辐射亮度值；Step S2, performing radiometric calibration on the SDGSAT-1 image of the area of interest, that is, converting the original DN value recorded in the SDGSAT-1 image into a radiometric brightness value;
步骤S3、对所述辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率；Step S3, performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into surface reflectivity;
步骤S4、根据所述地表反射率计算归一化水体指数；结合OTSU算法对所述归一化水体指数进行双峰阈值分割，提取水体；Step S4, calculating a normalized water index according to the surface reflectivity; performing bimodal threshold segmentation on the normalized water index in combination with the OTSU algorithm to extract the water body;
步骤S5、根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值；Step S5, calculating the classification index according to the surface reflectance of the image after the water body is extracted, and setting the index threshold;
步骤S6、根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。Step S6, according to the classification index and the index threshold, the surface reflectance of the image after the water body is extracted is used to calculate the suspended matter concentration index; and the suspended matter concentration is calculated using the suspended matter concentration index.
根据本发明第一方面的方法，在所述步骤S5中，所述根据提取水体后图像的地表反射率计算分类指标的方法包括：According to the method of the first aspect of the present invention, in step S5, the method of calculating the classification index according to the surface reflectance of the image after the water body is extracted includes:
根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标。The classification index is calculated based on the surface reflectance of the green band, red band, and blue band of the image after water body extraction.
根据本发明第一方面的方法，在所述步骤S5中，所述根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标的方法包括：According to the method of the first aspect of the present invention, in step S5, the method of calculating the classification index according to the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted includes:
其中，B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率。Among them, B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band.
根据本发明第一方面的方法，在所述步骤S5中，所述指标阈值等于0.56。According to the method of the first aspect of the present invention, in step S5, the indicator threshold is equal to 0.56.
根据本发明第一方面的方法，在所述步骤S6中，所述根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标的方法包括：According to the method of the first aspect of the present invention, in step S6, the method of calculating the suspended matter concentration index by applying the surface reflectance of the image after the water body is extracted according to the classification index and the index threshold comprises:
根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标。According to the classification index and the index threshold, the suspended matter concentration index is calculated by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted.
根据本发明第一方面的方法，在所述步骤S6中，所述根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标的方法包括：According to the method of the first aspect of the present invention, in step S6, the method of calculating the suspended matter concentration index by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted according to the classification index and the index threshold comprises:
若分类指标<指标阈值，If the classification index < index threshold,
若分类指标≥指标阈值，If the classification index ≥ index threshold,
其中，TI为悬浮物浓度指标；B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率。Among them, TI is the suspended matter concentration index; B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band.
根据本发明第一方面的方法，在所述步骤S6中，所述应用所述悬浮物浓度指标计算得出悬浮物浓度的方法包括：According to the method of the first aspect of the present invention, in step S6, the method of calculating the suspended matter concentration by applying the suspended matter concentration index includes:
TSM＝10TI TSM = 10 TI
其中，TI为悬浮物浓度指标；TSM为悬浮物浓度。Among them, TI is the suspended matter concentration index; TSM is the suspended matter concentration.
本发明第二方面公开了一种基于SDGSAT-1卫星的悬浮物浓度反演系统，所述系统包括：The second aspect of the present invention discloses a suspended matter concentration inversion system based on the SDGSAT-1 satellite, the system comprising:
第一处理模块，被配置为，在SDG大数据平台下载SDGSAT-1图像；在ENVI中将所述SDGSAT-1图像裁剪至感兴趣区域；The first processing module is configured to download the SDGSAT-1 image on the SDG big data platform; and crop the SDGSAT-1 image to the region of interest in ENVI;
第二处理模块，被配置为，对感兴趣区域的SDGSAT-1图像进行辐射定标，即将SDGSAT-1图像记录的原始DN值转换为辐射亮度值；The second processing module is configured to perform radiometric calibration on the SDGSAT-1 image of the area of interest, that is, convert the original DN value recorded in the SDGSAT-1 image into a radiometric brightness value;
第三处理模块，被配置为，对所述辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率；A third processing module is configured to perform atmospheric correction on the radiance value and convert the radiance value into surface reflectance;
第四处理模块，被配置为，根据所述地表反射率计算归一化水体指数；结合OTSU算法对所述归一化水体指数进行双峰阈值分割，提取水体；The fourth processing module is configured to calculate a normalized water index according to the surface reflectivity; perform bimodal threshold segmentation on the normalized water index in combination with the OTSU algorithm to extract water bodies;
第五处理模块，被配置为，根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值；The fifth processing module is configured to calculate a classification index according to the surface reflectance of the image after the water body is extracted, and set an index threshold;
第六处理模块，被配置为，根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。The sixth processing module is configured to calculate the suspended matter concentration index based on the classification index and the index threshold and apply the surface reflectance of the image after the water body is extracted; and calculate the suspended matter concentration using the suspended matter concentration index.
本发明第三方面公开了一种电子设备。电子设备包括存储器和处理器，存储器存储有计算机程序，处理器执行计算机程序时，实现本公开第一方面中任一项的一种基于SDGSAT-1卫星的悬浮物浓度反演方法中的步骤。The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the suspended matter concentration inversion methods based on the SDGSAT-1 satellite in the first aspect of the present disclosure are implemented.
本发明第四方面公开了一种计算机可读存储介质。计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时，实现本公开第一方面中任一项的一种基于SDGSAT-1卫星的悬浮物浓度反演方法中的步骤。The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the suspended matter concentration inversion methods based on the SDGSAT-1 satellite in the first aspect of the present disclosure are implemented.
综上，本发明提出的方案能够对常见的半解析算法在在重度浑浊水体上反演悬浮物浓度失效的现象进行了改进，对于不同浑浊程度的水体具有较高的适用性。In summary, the solution proposed in the present invention can improve the phenomenon that the common semi-analytical algorithm fails to invert the suspended matter concentration in severely turbid water bodies, and has high applicability to water bodies with different turbidity levels.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为根据本发明实施例的一种基于SDGSAT-1卫星的悬浮物浓度反演方法的流程图；FIG1 is a flow chart of a suspended matter concentration inversion method based on the SDGSAT-1 satellite according to an embodiment of the present invention;
图2为根据本发明实施例的一种基于SDGSAT-1卫星的悬浮物浓度反演系统的结构图；FIG2 is a structural diagram of a suspended matter concentration inversion system based on the SDGSAT-1 satellite according to an embodiment of the present invention;
图3为根据本发明实施例的一种电子设备的结构图。FIG. 3 is a structural diagram of an electronic device according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例只是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
本发明第一方面公开了一种基于SDGSAT-1卫星的悬浮物浓度反演方法。图1为根据本发明实施例的一种基于SDGSAT-1卫星的悬浮物浓度反演方法的流程图，如图1所示，所述方法包括：The first aspect of the present invention discloses a suspended matter concentration inversion method based on the SDGSAT-1 satellite. FIG1 is a flow chart of a suspended matter concentration inversion method based on the SDGSAT-1 satellite according to an embodiment of the present invention. As shown in FIG1 , the method comprises:
步骤S1、在SDG大数据平台下载SDGSAT-1图像；在ENVI中将所述SDGSAT-1图像裁剪至感兴趣区域；Step S1, downloading SDGSAT-1 images on the SDG big data platform; cropping the SDGSAT-1 images to the region of interest in ENVI;
步骤S2、对感兴趣区域的SDGSAT-1图像进行辐射定标，即将SDGSAT-1图像记录的原始DN值转换为辐射亮度值；Step S2, performing radiometric calibration on the SDGSAT-1 image of the area of interest, that is, converting the original DN value recorded in the SDGSAT-1 image into a radiometric brightness value;
步骤S3、对所述辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率；Step S3, performing atmospheric correction on the radiation brightness value, and converting the radiation brightness value into surface reflectivity;
步骤S4、根据所述地表反射率计算归一化水体指数；结合OTSU算法对所述归一化水体指数进行双峰阈值分割，提取水体；Step S4, calculating a normalized water index according to the surface reflectivity; performing bimodal threshold segmentation on the normalized water index in combination with the OTSU algorithm to extract the water body;
步骤S5、根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值；Step S5, calculating the classification index according to the surface reflectance of the image after the water body is extracted, and setting the index threshold;
步骤S6、根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。Step S6, according to the classification index and the index threshold, the surface reflectance of the image after the water body is extracted is used to calculate the suspended matter concentration index; and the suspended matter concentration is calculated using the suspended matter concentration index.
在步骤S1，在SDG大数据平台下载SDGSAT-1图像；在ENVI中将所述SDGSAT-1图像裁剪至感兴趣区域。In step S1, the SDGSAT-1 image is downloaded from the SDG big data platform; and the SDGSAT-1 image is cropped to the area of interest in ENVI.
具体地，MII多光谱影像包含7个波段，如表1所示。其中第3波段位于蓝光范围，第4波段位于绿光范围，第5波段位于红光波段，5、4、3的波段组合即SDGSAT-1多光谱图像RGB真彩色合成的波段组合。Specifically, the MII multispectral image contains 7 bands, as shown in Table 1. Band 3 is in the blue light range, band 4 is in the green light range, and band 5 is in the red light range. The band combination of 5, 4, and 3 is the band combination of RGB true color synthesis of SDGSAT-1 multispectral image.
表1Table 1
在步骤S2，对感兴趣区域的SDGSAT-1图像进行辐射定标，即将SDGSAT-1图像记录的原始DN值转换为辐射亮度值。In step S2, the SDGSAT-1 image of the area of interest is radiometrically calibrated, that is, the original DN value recorded in the SDGSAT-1 image is converted into a radiometric brightness value.
具体地，根据图像calib.xml文件给出的各波段增益系数gain和偏差Bias，计算得出辐亮度L，计算公式如下：Specifically, according to the gain coefficients and biases of each band given in the calib.xml file of the image, the radiance L is calculated. The calculation formula is as follows:
L＝gain*DN+Bias。L=gain*DN+Bias.
在步骤S3，对所述辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率。In step S3, atmospheric correction is performed on the radiance value to convert the radiance value into surface reflectivity.
具体地，Acolite(https://odnature.naturalsciences.be/remsem/software-and-data/acolite)是由RBINS(Royal Belgian Institute of Natural Sciences)提供的一种通用的大气校正处理器，可用于多种卫星任务的水上应用。Acolite可以简单快速地处理来自各种卫星的影像，包括Landsat(5/7/8/9)、Sentinel-2/MSI(A/B)、Sentinel-3/OLCI(A/B)、QuickBird2、WorldView-2等，以及几种针对沿海和内陆水体应用的高光谱传感器(CHRIS、HYPERION、HICO、PRISMA和DESIS)。近年，Acolite也补充了针对国产卫星SDGSAT-1多光谱传感仪(MII)的大气校正模块，为采用Acolite对SDGSAT-1影像进行大气校正提供了可能。本实施例采用Acolite模块对SDGSAT-1卫星影像进行大气校正。Specifically, Acolite (https://odnature.naturalsciences.be/remsem/software-and-data/acolite) is a general atmospheric correction processor provided by RBINS (Royal Belgian Institute of Natural Sciences) and can be used for aquatic applications of various satellite missions. 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 for coastal and inland water applications (CHRIS, HYPERION, HICO, PRISMA and DESIS). In recent years, Acolite has also supplemented the atmospheric correction module for the domestic satellite SDGSAT-1 multispectral sensor (MII), which makes it possible to use Acolite to perform atmospheric correction on SDGSAT-1 images. This embodiment uses the Acolite module to perform atmospheric correction on SDGSAT-1 satellite images.
Acolite处理器采用暗光谱拟合算法(Dark Spectrum Fitting，下称DSF)进行大气校正(Quinten Vanhellemont et al.,2018)。众所周知，在浑浊或是高生产率水体，近红外波段的反射率不可被忽略不计，因此DSF算法不假设任何一个波段具有可忽略的水体反射率，而是根据最低的天顶反射率从影像中寻找用于最优“暗”目标，即能够估算出最小程辐射的波段，甚至可能使用非水目标进行大气程辐射的估计。这一算法利用更多地面信息，因此能更好地应用于米级空间分辨率的传感器，以提高大气校正结果精度。研究表明，DSF算法特别适用于浑浊水体大气校正，同样很好地适用于清洁水体和陆地(QuintenVanhellemont et al.,2021)。The Acolite processor uses the Dark Spectrum Fitting (DSF) algorithm for atmospheric correction (Quinten Vanhellemont et al., 2018). It is well known that in turbid or highly productive water bodies, the reflectivity of the near-infrared band cannot be ignored. Therefore, the DSF algorithm does not assume that any band has negligible water reflectivity, but searches for the optimal "dark" target from the image based on the lowest zenith reflectivity, that is, the band that can estimate the minimum range radiation, and may even use non-water targets to estimate the atmospheric range radiation. This algorithm uses more ground information, so it can be better applied to sensors with a spatial resolution of meters to improve the accuracy of atmospheric correction results. Studies have shown that the DSF algorithm is particularly suitable for atmospheric correction of turbid water bodies, and is also well suited for clean water bodies and land (Quinten Vanhellemont et al., 2021).
在Github(https://github.com/acolite/acolite)上下载已封装好的Acolite模块，其中包含用于处理SDGSAT-1多光谱影像的sdgsat子模块，模块由python语言编写。编写python主函数，调用Acolite模块，运行大气校正。程序输入为包含多景SDGSAT-1影像组成的文件夹，输出为对应数量的、Acolite大气校正后的卫星影像组成的文件夹。Download the packaged Acolite module from Github (https://github.com/acolite/acolite), which contains the sdgsat submodule for processing SDGSAT-1 multispectral images. The module is written in Python. Write a Python main function, call the Acolite module, and run the atmospheric correction. The program input is a folder containing multiple SDGSAT-1 images, and the output is a folder containing the corresponding number of satellite images after Acolite atmospheric correction.
在步骤S4，根据所述地表反射率计算归一化水体指数；结合OTSU算法对所述归一化水体指数进行双峰阈值分割，提取水体。In step S4, a normalized water index is calculated according to the surface reflectivity; and a bimodal threshold segmentation is performed on the normalized water index in combination with the OTSU algorithm to extract the water body.
具体地，由于河流分布比较破碎，尤其是对于形状蜿蜒复杂的河流，受陆地临近像元影响较大。因此，水体范围提取的精度直接影响到水质反演的精度和效果。并且，只有实现自动化提取才能满足大区域尺度、长时序的水质研究需求。Specifically, due to the fragmented distribution of rivers, especially for rivers with complex winding shapes, they are greatly affected by the pixels adjacent to the land. Therefore, the accuracy of water body range extraction directly affects the accuracy and effect of water quality inversion. Moreover, only automated extraction can meet the needs of large-scale and long-term water quality research.
Mcfeeters于1996年提出归一化水体指数NDWI，该指数是利用波段差异比值构建的水体信息提取方法。水体对近红外具有强吸收性，陆地与植被在近红外波段反射率明显增强，波谱选择在绿光和近红外通道范围，突出了水体与其他地物的差异(Mcfeeters,1996)。NDWI计算公式如下：Mcfeeters proposed the Normalized Difference Water Index (NDWI) in 1996. This index is a water body information extraction method constructed using the band difference ratio. Water bodies have strong absorption of near-infrared, and the reflectivity of land and vegetation in the near-infrared band is significantly enhanced. The spectrum is selected in the green light and near-infrared channel range, highlighting the difference between water bodies and other landforms (Mcfeeters, 1996). The NDWI calculation formula is as follows:
其中，NDWI为归一化水体指数；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B7为SDGSAT-1图像的第七波段的地表反射率，即近红外波段的地表反射率。Among them, NDWI is the normalized water index; B4 is the surface reflectance of the fourth band of the SDGSAT-1 image, that is, the surface reflectance of the green band; B7 is the surface reflectance of the seventh band of the SDGSAT-1 image, that is, the surface reflectance of the near-infrared band.
该水体指数对于水体周边的干扰因素具有很好的抑制效果，所以使用该指数提取水体。通常情况，基于水体指数提取水体是计算最优阈值或人为选取经验阈值区分每景遥感影像的水体与非水体部分。本发明根据水体的一般矢量边界向外扩展1.5倍面积，在此范围内确定阈值。针对大量长时序遥感数据，手动确定阈值需要耗费大量时间，采用统一的经验阈值又无法使每景影像的水体提取精度达到最佳。因此，本实施例结合OTSU算法对NDWI进行双峰阈值分割，实现逐景影像水体阈值自动化确定，更加高效、精确、自动化的区分每景遥感影像的水体与非水体部分。OTSU算法又称最大类间方差法，是在最小二乘原理的基础上推导而来的。其基本思想是基于影像的直方图计算各灰度级的发生概率，并以某一阈值变量t将构成图像的所有像素分为两类，然后求取每一类的类间方差，选取使得两组类间方差最大时的t值，作为二值化处理的最佳阈值。该算法能根据不同的影像的直方图自适应地确定每景影像的最佳阈值(黄广才,2019)。因此，NDWI结合OTSU算法能够有效地实现大范围、长时序的水体提取，相对于全局统一阈值来说，一定程度上会提高水体提取的精度。The water body index has a good inhibitory effect on the interference factors around the water body, so the index is used to extract the water body. Generally, the extraction of water bodies based on the water body index is to calculate the optimal threshold or artificially select the empirical threshold to distinguish the water body and non-water body parts of each remote sensing image. The present invention expands 1.5 times the area outward according to the general vector boundary of the water body, and determines the threshold within this range. For a large amount of long-time series remote sensing data, it takes a lot of time to manually determine the threshold, and the use of a unified empirical threshold cannot make the water body extraction accuracy of each image reach the best. Therefore, this embodiment combines the OTSU algorithm to perform bimodal threshold segmentation on NDWI, realizes the automatic determination of the water body threshold of each image, and distinguishes the water body and non-water body parts of each remote sensing image more efficiently, accurately and automatically. The OTSU algorithm, also known as the maximum inter-class variance method, is derived on the basis of the least squares principle. Its basic idea is to calculate the probability of occurrence of each gray level based on the histogram of the image, and divide all the pixels constituting the image into two categories with a certain threshold variable t, and then obtain the inter-class variance of each category, and select the t value when the inter-class variance of the two groups is the largest as the optimal threshold for binarization. The algorithm can adaptively determine the optimal threshold for each image based on the histogram of different images (Huang Guangcai, 2019). Therefore, NDWI combined with the OTSU algorithm can effectively achieve large-scale and long-time water extraction, which can improve the accuracy of water extraction to a certain extent compared with the global unified threshold.
在步骤S5，根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值。In step S5, a classification index is calculated according to the surface reflectance of the image after the water body is extracted, and an index threshold is set.
在一些实施例中，在所述步骤S5中，所述根据提取水体后图像的地表反射率计算分类指标的方法包括：In some embodiments, in step S5, the method of calculating the classification index according to the surface reflectance of the image after the water body is extracted includes:
根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标。The classification index is calculated based on the surface reflectance of the green band, red band, and blue band of the image after water body extraction.
所述根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标的方法包括：The method for calculating the classification index according to the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted includes:
其中，B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率；Among them, B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band;
所述指标阈值等于0.56。The indicator threshold is equal to 0.56.
在步骤S6，根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。In step S6, according to the classification index and the index threshold, the surface reflectance of the image after the water body is extracted is used to calculate the suspended matter concentration index; and the suspended matter concentration is calculated using the suspended matter concentration index.
在一些实施例中，在所述步骤S6中，所述根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标的方法包括：In some embodiments, in step S6, the method of calculating the suspended matter concentration index by applying the surface reflectance of the image after water body extraction according to the classification index and the index threshold comprises:
根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标。According to the classification index and the index threshold, the suspended matter concentration index is calculated by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted.
所述根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标的方法包括：The method for calculating the suspended matter concentration index according to the classification index and the index threshold by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted includes:
若分类指标<指标阈值，If the classification index < index threshold,
若分类指标≥指标阈值，If the classification index ≥ index threshold,
其中，TI为悬浮物浓度指标；B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率。Among them, TI is the suspended matter concentration index; B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band.
所述应用所述悬浮物浓度指标计算得出悬浮物浓度的方法包括：The method for calculating the suspended matter concentration by applying the suspended matter concentration index includes:
TSM＝10TI TSM = 10 TI
其中，TI为悬浮物浓度指标；TSM为悬浮物浓度TSM(mg/L)。Among them, TI is the suspended matter concentration index; TSM is the suspended matter concentration TSM (mg/L).
综上，本发明提出的方案能够常见的半解析算法在在重度浑浊水体上反演悬浮物浓度失效的现象进行了改进，对于不同浑浊程度的水体具有较高的适用性；具有同类大气校正方法中较高的精度，且能更好地应用于米级空间分辨率的传感器，这提高了利用高分辨率卫星数据进行大范围长时序水质监测的精度和可靠性。In summary, the solution proposed in the present invention can improve the failure of common semi-analytical algorithms in inverting suspended matter concentration in heavily turbid water bodies, and has high applicability for water bodies with different turbidity levels; it has higher accuracy among similar atmospheric correction methods, and can be better applied to sensors with meter-level spatial resolution, which improves the accuracy and reliability of large-scale and long-term water quality monitoring using high-resolution satellite data.
本发明第二方面公开了一种基于SDGSAT-1卫星的悬浮物浓度反演系统。图2为根据本发明实施例的一种基于SDGSAT-1卫星的悬浮物浓度反演系统的结构图；如图2所示，所述系统100包括：The second aspect of the present invention discloses a suspended matter concentration inversion system based on the SDGSAT-1 satellite. FIG2 is a structural diagram of a suspended matter concentration inversion system based on the SDGSAT-1 satellite according to an embodiment of the present invention; as shown in FIG2 , the system 100 includes:
第一处理模块101，被配置为，在SDG大数据平台下载SDGSAT-1图像；在ENVI中将所述SDGSAT-1图像裁剪至感兴趣区域；The first processing module 101 is configured to download the SDGSAT-1 image on the SDG big data platform; and crop the SDGSAT-1 image to the region of interest in ENVI;
第二处理模块102，被配置为，对感兴趣区域的SDGSAT-1图像进行辐射定标，即将SDGSAT-1图像记录的原始DN值转换为辐射亮度值；The second processing module 102 is configured to perform radiometric calibration on the SDGSAT-1 image of the area of interest, that is, convert the original DN value recorded in the SDGSAT-1 image into a radiometric brightness value;
第三处理模块103，被配置为，对所述辐射亮度值进行大气校正，将辐射亮度值转换为地表反射率；The third processing module 103 is configured to perform atmospheric correction on the radiance value and convert the radiance value into surface reflectivity;
第四处理模块104，被配置为，根据所述地表反射率计算归一化水体指数；结合OTSU算法对所述归一化水体指数进行双峰阈值分割，提取水体；The fourth processing module 104 is configured to calculate a normalized water index according to the surface reflectivity; perform bimodal threshold segmentation on the normalized water index in combination with the OTSU algorithm to extract water bodies;
第五处理模块105，被配置为，根据提取水体后图像的地表反射率计算分类指标，并设置指标阈值；The fifth processing module 105 is configured to calculate a classification index according to the surface reflectance of the image after the water body is extracted, and set an index threshold;
第六处理模块106，被配置为，根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标；应用所述悬浮物浓度指标计算得出悬浮物浓度。The sixth processing module 106 is configured to calculate the suspended matter concentration index based on the classification index and the index threshold and apply the surface reflectance of the image after the water body is extracted; and calculate the suspended matter concentration using the suspended matter concentration index.
根据本发明第二方面的系统，所述第一处理模块101具体被配置为，MII多光谱影像包含7个波段，如表1所示。其中第3波段位于蓝光范围，第4波段位于绿光范围，第5波段位于红光波段，5、4、3的波段组合即SDGSAT-1多光谱图像RGB真彩色合成的波段组合。According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured as follows: the MII multispectral image includes 7 bands, as shown in Table 1. The third band is located in the blue light range, the fourth band is located in the green light range, and the fifth band is located in the red light band. The band combination of 5, 4, and 3 is the band combination of the RGB true color synthesis of the SDGSAT-1 multispectral image.
表1Table 1
根据本发明第二方面的系统，所述第二处理模块102具体被配置为，根据图像calib.xml文件给出的各波段增益系数gain和偏差Bias，计算得出辐亮度L，计算公式如下：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 bias Bias of each band given in the image calib.xml file, and the calculation formula is as follows:
L＝gain*DN+Bias。L=gain*DN+Bias.
根据本发明第二方面的系统，所述第三处理模块103具体被配置为，Acolite(https://odnature.naturalsciences.be/remsem/software-and-data/acolite)是由RBINS(Royal Belgian Institute of Natural Sciences)提供的一种通用的大气校正处理器，可用于多种卫星任务的水上应用。Acolite可以简单快速地处理来自各种卫星的影像，包括Landsat(5/7/8/9)、Sentinel-2/MSI(A/B)、Sentinel-3/OLCI(A/B)、QuickBird2、WorldView-2等，以及几种针对沿海和内陆水体应用的高光谱传感器(CHRIS、HYPERION、HICO、PRISMA和DESIS)。近年，Acolite也补充了针对国产卫星SDGSAT-1多光谱传感仪(MII)的大气校正模块，为采用Acolite对SDGSAT-1影像进行大气校正提供了可能。本实施例采用Acolite模块对SDGSAT-1卫星影像进行大气校正。According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured as, Acolite (https://odnature.naturalsciences.be/remsem/software-and-data/acolite) is a general atmospheric correction processor provided by RBINS (Royal Belgian Institute of Natural Sciences), which can be used for aquatic applications of various satellite missions. 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 for coastal and inland water applications (CHRIS, HYPERION, HICO, PRISMA and DESIS). In recent years, Acolite has also supplemented the atmospheric correction module for the domestic satellite SDGSAT-1 multispectral sensor (MII), which makes it possible to use Acolite to perform atmospheric correction on SDGSAT-1 images. This embodiment uses the Acolite module to perform atmospheric correction on SDGSAT-1 satellite images.
Acolite处理器采用暗光谱拟合算法(Dark Spectrum Fitting，下称DSF)进行大气校正(Quinten Vanhellemont et al.,2018)。众所周知，在浑浊或是高生产率水体，近红外波段的反射率不可被忽略不计，因此DSF算法不假设任何一个波段具有可忽略的水体反射率，而是根据最低的天顶反射率从影像中寻找用于最优“暗”目标，即能够估算出最小程辐射的波段，甚至可能使用非水目标进行大气程辐射的估计。这一算法利用更多地面信息，因此能更好地应用于米级空间分辨率的传感器，以提高大气校正结果精度。研究表明，DSF算法特别适用于浑浊水体大气校正，同样很好地适用于清洁水体和陆地(QuintenVanhellemont et al.,2021)。The Acolite processor uses the Dark Spectrum Fitting (DSF) algorithm for atmospheric correction (Quinten Vanhellemont et al., 2018). It is well known that in turbid or highly productive water bodies, the reflectivity of the near-infrared band cannot be ignored. Therefore, the DSF algorithm does not assume that any band has negligible water reflectivity, but searches for the optimal "dark" target from the image based on the lowest zenith reflectivity, that is, the band that can estimate the minimum range radiation, and may even use non-water targets to estimate the atmospheric range radiation. This algorithm uses more ground information, so it can be better applied to sensors with a spatial resolution of meters to improve the accuracy of atmospheric correction results. Studies have shown that the DSF algorithm is particularly suitable for atmospheric correction of turbid water bodies, and is also well suited for clean water bodies and land (Quinten Vanhellemont et al., 2021).
在Github(https://github.com/acolite/acolite)上下载已封装好的Acolite模块，其中包含用于处理SDGSAT-1多光谱影像的sdgsat子模块，模块由python语言编写。编写python主函数，调用Acolite模块，运行大气校正。程序输入为包含多景SDGSAT-1影像组成的文件夹，输出为对应数量的、Acolite大气校正后的卫星影像组成的文件夹。Download the packaged Acolite module from Github (https://github.com/acolite/acolite), which contains the sdgsat submodule for processing SDGSAT-1 multispectral images. The module is written in Python. Write a Python main function, call the Acolite module, and run the atmospheric correction. The program input is a folder containing multiple SDGSAT-1 images, and the output is a folder containing the corresponding number of satellite images after Acolite atmospheric correction.
根据本发明第二方面的系统，所述第四处理模块104具体被配置为，由于河流分布比较破碎，尤其是对于形状蜿蜒复杂的河流，受陆地临近像元影响较大。因此，水体范围提取的精度直接影响到水质反演的精度和效果。并且，只有实现自动化提取才能满足大区域尺度、长时序的水质研究需求。According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured as follows: since the distribution of rivers is relatively fragmented, especially for rivers with complex winding shapes, they are greatly affected by the pixels adjacent to the land. Therefore, the accuracy of water body range extraction directly affects the accuracy and effect of water quality inversion. Moreover, only by realizing automated extraction can the needs of large-scale and long-time series water quality research be met.
Mcfeeters于1996年提出归一化水体指数NDWI，该指数是利用波段差异比值构建的水体信息提取方法。水体对近红外具有强吸收性，陆地与植被在近红外波段反射率明显增强，波谱选择在绿光和近红外通道范围，突出了水体与其他地物的差异(Mcfeeters,1996)。NDWI计算公式如下：Mcfeeters proposed the Normalized Difference Water Index (NDWI) in 1996. This index is a water information extraction method constructed using the band difference ratio. Water bodies have strong absorption of near-infrared, and the reflectivity of land and vegetation in the near-infrared band is significantly enhanced. The spectrum is selected in the green light and near-infrared channel range, highlighting the difference between water bodies and other landforms (Mcfeeters, 1996). The NDWI calculation formula is as follows:
其中，NDWI为归一化水体指数；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B7为SDGSAT-1图像的第七波段的地表反射率，即近红外波段的地表反射率。Among them, NDWI is the normalized water index; B4 is the surface reflectance of the fourth band of the SDGSAT-1 image, that is, the surface reflectance of the green band; B7 is the surface reflectance of the seventh band of the SDGSAT-1 image, that is, the surface reflectance of the near-infrared band.
该水体指数对于水体周边的干扰因素具有很好的抑制效果，所以使用该指数提取水体。通常情况，基于水体指数提取水体是计算最优阈值或人为选取经验阈值区分每景遥感影像的水体与非水体部分。本发明根据水体的一般矢量边界向外扩展1.5倍面积，在此范围内确定阈值。针对大量长时序遥感数据，手动确定阈值需要耗费大量时间，采用统一的经验阈值又无法使每景影像的水体提取精度达到最佳。因此，本实施例结合OTSU算法对NDWI进行双峰阈值分割，实现逐景影像水体阈值自动化确定，更加高效、精确、自动化的区分每景遥感影像的水体与非水体部分。OTSU算法又称最大类间方差法，是在最小二乘原理的基础上推导而来的。其基本思想是基于影像的直方图计算各灰度级的发生概率，并以某一阈值变量t将构成图像的所有像素分为两类，然后求取每一类的类间方差，选取使得两组类间方差最大时的t值，作为二值化处理的最佳阈值。该算法能根据不同的影像的直方图自适应地确定每景影像的最佳阈值(黄广才,2019)。因此，NDWI结合OTSU算法能够有效地实现大范围、长时序的水体提取，相对于全局统一阈值来说，一定程度上会提高水体提取的精度。The water body index has a good inhibitory effect on the interference factors around the water body, so the index is used to extract the water body. Generally, the extraction of water bodies based on the water body index is to calculate the optimal threshold or artificially select the empirical threshold to distinguish the water body and non-water body parts of each remote sensing image. The present invention expands 1.5 times the area outward according to the general vector boundary of the water body, and determines the threshold within this range. For a large amount of long-time series remote sensing data, it takes a lot of time to manually determine the threshold, and the use of a unified empirical threshold cannot make the water body extraction accuracy of each image reach the best. Therefore, this embodiment combines the OTSU algorithm to perform bimodal threshold segmentation on NDWI, realizes the automatic determination of the water body threshold of each image, and distinguishes the water body and non-water body parts of each remote sensing image more efficiently, accurately and automatically. The OTSU algorithm, also known as the maximum inter-class variance method, is derived on the basis of the least squares principle. Its basic idea is to calculate the probability of occurrence of each gray level based on the histogram of the image, and divide all the pixels constituting the image into two categories with a certain threshold variable t, and then obtain the inter-class variance of each category, and select the t value when the inter-class variance of the two groups is the largest as the optimal threshold for binarization. The algorithm can adaptively determine the optimal threshold for each image based on the histogram of different images (Huang Guangcai, 2019). Therefore, NDWI combined with the OTSU algorithm can effectively achieve large-scale and long-time water extraction, which can improve the accuracy of water extraction to a certain extent compared with the global unified threshold.
根据本发明第二方面的系统，所述第五处理模块105具体被配置为，所述根据提取水体后图像的地表反射率计算分类指标的方法包括：According to the system of the second aspect of the present invention, the fifth processing module 105 is specifically configured as follows: the method for calculating the classification index according to the surface reflectance of the image after the water body is extracted includes:
根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标。The classification index is calculated based on the surface reflectance of the green band, red band, and blue band of the image after water body extraction.
所述根据提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算分类指标的方法包括：The method for calculating the classification index according to the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted includes:
其中，B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率；Among them, B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band;
所述指标阈值等于0.56。The indicator threshold is equal to 0.56.
根据本发明第二方面的系统，所述第六处理模块106具体被配置为，所述根据所述分类指标和指标阈值，应用提取水体后图像的地表反射率，计算悬浮物浓度指标的方法包括：According to the system of the second aspect of the present invention, the sixth processing module 106 is specifically configured as follows: the method of calculating the suspended matter concentration index by applying the surface reflectance of the image after the water body is extracted according to the classification index and the index threshold comprises:
根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标。According to the classification index and the index threshold, the suspended matter concentration index is calculated by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted.
所述根据所述分类指标和指标阈值，应用提取水体后图像的绿波段、红波段和蓝波段的地表反射率，计算悬浮物浓度指标的方法包括：The method of calculating the suspended matter concentration index according to the classification index and the index threshold by applying the surface reflectance of the green band, the red band and the blue band of the image after the water body is extracted includes:
若分类指标<指标阈值，If the classification index < index threshold,
若分类指标≥指标阈值，If the classification index ≥ index threshold,
其中，TI为悬浮物浓度指标；B3为SDGSAT-1图像的第三波段的地表反射率，即蓝波段的地表反射率；B4为SDGSAT-1图像的第四波段的地表反射率，即绿波段的地表反射率；B5为SDGSAT-1图像的第五波段的地表反射率，即红波段的地表反射率。Among them, TI is the suspended matter concentration index; B3 is the surface reflectivity of the third band of the SDGSAT-1 image, that is, the surface reflectivity of the blue band; B4 is the surface reflectivity of the fourth band of the SDGSAT-1 image, that is, the surface reflectivity of the green band; B5 is the surface reflectivity of the fifth band of the SDGSAT-1 image, that is, the surface reflectivity of the red band.
所述应用所述悬浮物浓度指标计算得出悬浮物浓度的方法包括：The method for calculating the suspended matter concentration by applying the suspended matter concentration index includes:
TSM＝10TI TSM = 10 TI
其中，TI为悬浮物浓度指标；TSM为悬浮物浓度TSM(mg/L)。Among them, TI is the suspended matter concentration index; TSM is the suspended matter concentration TSM (mg/L).
本发明第三方面公开了一种电子设备。电子设备包括存储器和处理器，存储器存储有计算机程序，处理器执行计算机程序时，实现本发明公开第一方面中任一项的一种基于SDGSAT-1卫星的悬浮物浓度反演方法中的步骤。The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the suspended matter concentration inversion methods based on the SDGSAT-1 satellite disclosed in the first aspect of the present invention are implemented.
图3为根据本发明实施例的一种电子设备的结构图，如图3所示，电子设备包括通过系统总线连接的处理器、存储器、通信接口、显示屏和输入装置。其中，该电子设备的处理器用于提供计算和控制能力。该电子设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该电子设备的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、运营商网络、近场通信(NFC)或其他技术实现。该电子设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该电子设备的输入装置可以是显示屏上覆盖的触摸层，也可以是电子设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。FIG3 is a block diagram of an electronic device according to an embodiment of the present invention. As shown in FIG3 , the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile 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 the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, near field communication (NFC) or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse, etc.
本领域技术人员可以理解，图3中示出的结构，仅仅是与本公开的技术方案相关的部分的结构图，并不构成对本申请方案所应用于其上的电子设备的限定，具体的电子设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。Those skilled in the art will understand that the structure shown in FIG. 3 is merely a structural diagram of the portion related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the technical solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
本发明第四方面公开了一种计算机可读存储介质。计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时，实现本发明公开第一方面中任一项的一种基于SDGSAT-1卫星的悬浮物浓度反演方法中的步骤。The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the suspended matter concentration inversion methods based on the SDGSAT-1 satellite disclosed in the first aspect of the present invention are implemented.
请注意，以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。以上实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出若干变形和改进，这些都属于本申请的保护范围。因此，本申请专利的保护范围应以所附权利要求为准。Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.
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