CN115452759A - River and lake health index evaluation method and system based on satellite remote sensing data - Google Patents
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Abstract
The invention provides a method and a system for evaluating river and lake health indexes based on satellite remote sensing data, and belongs to the technical field of remote sensing data processing and analysis. The method specifically comprises the following steps: step 1, reading remote sensing index analysis data in a database based on a research period; step 2, constructing an index evaluation model and receiving read analysis data of the remote sensing indexes; and 3, evaluating the health condition of the rivers and the lakes based on the analysis result of the index evaluation model. According to the invention, through the capability of the high-resolution remote sensing satellite in the aspects of large scale, all weather, strong real-time performance and the like, the river and lake health evaluation cost is effectively reduced, the timeliness of evaluation is improved, the supervision difficulty is reduced, and meanwhile, the technical support is provided for the subsequent overall supervision of the ecological health change of river and lake objects.
Description
Technical Field
The invention relates to the technical field of remote sensing data processing and analysis, in particular to a river and lake health index evaluation method and system based on satellite remote sensing data.
Background
Rivers in different regions have great difference on the background of natural geography and economic society, and the phenomenon of uneven water resource distribution exists in the aspect of water consumption due to different population distribution densities near the river basin. With the rapid development of economic society, the utilization strength of water resources is continuously increased, the ecological systems of rivers and lakes are gradually damaged and even destroyed, the problems of water pollution, deterioration of hydrological conditions, destruction of morphological structures of rivers and lakes, damage of biodiversity, degradation of ecological functions of rivers and lakes and the like generally occur in the global range, and the method has a serious effect on the economic development.
In the prior art, a river and lake health concept is defined by focusing basins and water of rivers and lakes, and an evaluation system is established, but in the actual application process, the evaluation method has certain access due to the problems of adaptability, convenience and the like.
Disclosure of Invention
The purpose of the invention is as follows: a method and a system for evaluating river and lake health indexes based on satellite remote sensing data are provided to solve the problems in the prior art. By utilizing the observation capability of the high-resolution remote sensing satellite in the aspects of large scale, all-weather, quasi-real time and the like, the dynamic evaluation and supervision application research of the health of rivers and lakes is developed, and the dynamic evaluation of single indexes and health conditions of the health of the rivers and lakes is realized, so that the evaluation cost of the health of the rivers and lakes is effectively reduced, the evaluation efficiency is improved, the supervision difficulty is reduced, and meanwhile, the technical support is provided for the follow-up supervision of the whole process of the ecological health change of the river and lake objects.
The technical scheme is as follows: the first aspect provides a river and lake health index evaluation method based on satellite remote sensing data, which specifically comprises the following steps:
step 1, constructing a satellite remote sensing data storage database;
step 2, reading remote sensing index analysis data in a satellite remote sensing data storage database within a preset time period and a space range through a data reading module;
step 3, constructing an index evaluation model by a model construction module, and receiving read remote sensing index analysis data;
and 4, analyzing data based on the remote sensing indexes read in the step 3, and evaluating the health conditions of the rivers and the lakes by an evaluation module.
In some implementations of the first aspect, the method for comprehensively evaluating the health condition of the rivers and the lakes based on the analysis result of the index evaluation model further includes the following steps:
step 3.1, analyzing the vegetation coverage rate of the river and lake shoreline;
step 3.2, acquiring the area shrinkage proportion of the lake;
and 3.3, comparing the appearance conditions of the rivers and the lakes.
The method specifically comprises the following steps of when the vegetation coverage rate of the river, lake and shoreline is analyzed:
step 3.1.1, segmenting the river and lake shoreline according to a preset distance;
step 3.1.2, calculating the vegetation coverage rate of each bank section according to the sections;
step 3.1.3, carrying out weight weighting calculation on the length of the opposite bank segment based on the whole bank line;
and 3.1.4, acquiring the vegetation coverage of the whole shoreline through weighted summation.
When the lake area shrinkage proportion is obtained, the method specifically comprises the following steps:
step 3.2.1, obtaining historical data;
step 3.2.2, acquiring the water body range in the research area through the difference of the illumination wave bands;
and 3.2.3, calculating the lake shrinkage proportion of the research area by adopting a proportion mode based on the historical data.
When the river and lake appearance presenting conditions are compared, the method specifically comprises the following steps:
step 3.3.1, obtaining remote sensing image data of a research area;
step 3.3.2, preprocessing the remote sensing image data to obtain a test image;
3.3.3, constructing a segmentation model, and converting the test image of the target area into an image with a corresponding class label by using the segmentation model;
3.3.4, extracting the outline of the research area based on the converted image data;
and 3.3.5, analyzing the image characteristics in the research area, comparing the image characteristics with the historical characteristics of the research area, generating a final comparison result and outputting the final comparison result.
In some implementations of the first aspect, the process of obtaining the test image of the target area specifically includes the following steps:
step 3.3.2.1, reading remote sensing data;
step 3.3.2.2, acquiring a format which accords with the required image data through data preprocessing;
step 3.3.2.3, constructing a reflection matrix according to the image data format;
and 3.3.2.4, constructing a projection coordinate system, and generating a test image according to a preset wave band.
The process of converting the test image of the target area into the image with the corresponding class label by using the segmentation model specifically comprises the following steps:
step 3.3.3.1, obtaining a test image;
step 3.3.3.2, labeling each pixel in the test image;
3.3.3.3, slicing the test image according to a preset specification to obtain a pixel block;
step 3.3.3.4, dividing the pixel block into a verification data set and a test set according to the proportion;
3.3.3.5, constructing a segmentation model, and performing model training by using the verification data set and the test set;
and 3.3.3.6, converting the test image of the target area into an image corresponding to the class label by using the trained segmentation model.
The process of extracting the contour of the study region based on the converted image data specifically includes the following steps:
step 3.3.4.1, carrying out binarization processing on various labels in the test image;
3.3.4.2, selecting pixel points corresponding to the labels as target pixel points according to requirements, defining the numerical value of the target pixel points as a first numerical value, and defining the residual pixel point value as a second numerical value;
step 3.3.4.3, grouping the binary class labels to regionalize adjacent and same-class pixel points;
and 3.3.4.4, obtaining the coordinates of points at the boundary of the area and forming a contour according to the difference between the pixel values in the area and the residual pixel values.
In a second aspect, a river and lake health index evaluation system based on satellite remote sensing data is provided, and the system specifically comprises the following modules:
the data reading module is used for acquiring analysis data of the remote sensing indexes;
the model building module is used for building an index evaluation model;
and the evaluation module is used for evaluating the health condition of the rivers and the lakes.
In a third aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, the corresponding computer program instructions being executed by a processor to implement an index evaluation method.
Has the advantages that: the invention provides a method and a system for evaluating river and lake health indexes based on satellite remote sensing data, which effectively reduce river and lake health evaluation cost, improve evaluation timeliness and reduce supervision difficulty through the capacity of a high-resolution remote sensing satellite in the aspects of large scale, all weather, strong real-time performance and the like, and simultaneously provide technical support for the follow-up supervision of the whole ecological health change process of river and lake objects.
Drawings
FIG. 1 is a flow chart of data processing according to the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
With the emphasis on ecological environment, the evaluation of the corresponding water environment index gradually becomes a life index which is not negligible in public life, and in the prior art, the acquisition mode of the index to be evaluated is generally a field detection mode, a manual visual inspection mode and the like, so that the problems of timeliness and great difficulty in detection exist. Therefore, the river and lake health index evaluation method and system based on satellite remote sensing data effectively reduce river and lake health evaluation cost, improve evaluation timeliness and reduce supervision difficulty through the capacity of the high-resolution remote sensing satellite in the aspects of large scale, all weather, strong real-time performance and the like, and meanwhile provide technical support for the subsequent overall supervision of river and lake object ecological health change.
Example one
In an embodiment, a method for evaluating river and lake health indexes based on satellite remote sensing data is provided, as shown in fig. 1, the method specifically includes the following steps:
step 1, constructing a satellite remote sensing data storage database;
step 2, reading remote sensing index analysis data in a satellite remote sensing data storage database within a preset time period and a space range through a data reading module;
step 3, constructing an index evaluation model by a model construction module, and receiving read remote sensing index analysis data;
and 4, analyzing data based on the remote sensing indexes read in the step 3, and evaluating the health conditions of the rivers and the lakes by using an evaluation module.
In a further embodiment, the indexes for evaluating the health of rivers and lakes specifically include: coverage rate of river and lake shoreline vegetation, shrinkage proportion of lake area and appearance condition of river and lake.
Specifically, when the vegetation coverage of the river and lake shoreline is obtained, the river and lake shoreline is segmented according to a preset distance, and the vegetation coverage of each shoreline is calculated according to the divided shoreline; and then carrying out weight weighting calculation on the lengths of the opposite bank segments based on the whole shoreline to obtain the vegetation coverage rate of the whole shoreline of the river and lake, wherein the corresponding expression is as follows:
in the formula, PC r Representing the evaluation score of the vegetation coverage of the shoreline; a. The ci Representing the vegetation coverage area of the bank section i; a. The ai Representing the zone area of bank segment i; l is a radical of an alcohol vci Represents the length of the land section i; l represents the total length of the evaluation bank segment; n represents the total number of bank segments within the evaluation range.
When the lake area shrinkage proportion is calculated, the proportion of the lake surface shrinkage area of the evaluation year lake to the lake surface area of the historical reference year lake is adopted for representation, and the corresponding calculation expression is as follows:
in the formula, ASI represents the shrinkage proportion of the lake area; AC represents the lake surface area of the evaluation year; AR represents the historic reference annual lake surface area.
When the appearance conditions of rivers and lakes are judged, remote sensing data are adopted to carry out multi-period time sequence monitoring, image spot extraction is carried out on behaviors such as soil movement, buildings and piled materials, and the like, and the behavior is compared and analyzed with historical reference period data, so that change data is obtained, and manual judgment is assisted to carry out four-disorder condition judgment.
The river and lake health conditions in the research field are analyzed by constructing the index evaluation model, and the ecological condition of the research field is effectively obtained, so that an effective analysis basis is provided for subsequent environmental improvement. Meanwhile, based on the capability of the high-resolution remote sensing satellite in the aspects of large scale, all weather, strong real-time performance and the like, the river and lake health evaluation cost is effectively reduced, the timeliness of evaluation is improved, the supervision difficulty is reduced, and meanwhile, the technical support is provided for the subsequent overall supervision of the ecological health change of river and lake objects.
Example two
In a further embodiment based on the first embodiment, since the surface vegetation exhibits horizontal and vertical distributed growth conditions, and the colors and shapes of different types of vegetation are different, the present embodiment is mainly obtained by changing the features and differences of the vegetation spectral bands based on the information about the vegetation in the remote sensing image, and the features of different vegetation are identified and analyzed through different spectral bands. In the process of analyzing the vegetation coverage rate of the river and lake shoreline, a vegetation index method is adopted for extracting the vegetation coverage condition of the river and lake shoreline.
Specifically, the difference of chlorophyll absorption between a green-leaf near-infrared band spectrum channel and a red-leaf band spectrum channel in the vegetation is increased, the vegetation coverage is estimated through the difference, and the vegetation information is obtained. The vegetation index expression is obtained as follows:
wherein NDVI has a value in the range of-1 to 1; IR represents the reflectance of the near infrared band; r represents an infrared band reflectance. When the value of the NDVI is less than 0, the NDVI represents that the ground is composed of water, snow, glacier and the like; when the value of NDVI is 0, the NDVI represents the components of desert, rock or bare ground on the ground; and when the value of the NDVI is greater than 0, indicating that the vegetation is on the ground. The normalized vegetation index and the vegetation coverage degree are in a positive correlation relationship, wherein the value range of the green vegetation index is 0.2 to 0.8.
Obtaining raster data by utilizing a computational expression of NDVI (normalized difference of variance) to perform raster extraction of green vegetation indexes, further obtaining the coverage range of green vegetation in an evaluation range, and then obtaining a final shoreline vegetation coverage evaluation score PC (personal computer) after one-by-one operation r 。
In the preferred embodiment, the vegetation coverage index of the river and lake shoreline is selected from the months with the most vigorous plant growth in 3-10 months for investigation, and compared with the mode of on-site investigation or artificial visual interpretation of the large-scale river and lake shoreline in the prior art, the method in the embodiment reduces the requirement on a large amount of manpower, improves the interpretation efficiency to a large extent, improves the observation frequency and the efficiency, and provides technical support for intelligent river and lake health monitoring.
The vegetation index adopted by the embodiment is used for obtaining the vegetation coverage of the river and lake shoreline, the vegetation coverage is estimated by combining different spectral bands and different calculation methods, and meanwhile, the spectral band with large information amount and weak correlation is selected for estimating the vegetation coverage, so that the spectral band is extracted for analysis without large deviation, and the vegetation coverage can be ensured to have high accuracy. In addition, the method of normalizing the vegetation index is adopted, so that the requirements of high efficiency and rapidity in extraction and research of the vegetation coverage of the area are met, and the method is not limited by the area and time.
EXAMPLE III
In a further embodiment based on the embodiment, in the process of calculating the shrinkage ratio of the lake area, the historical reference lake water surface area is obtained by inquiring the historical yearbook, and when the related historical records cannot be inquired, manual approval is carried out by obtaining the time-lapse image. In the preferred embodiment, when historical data acquisition history refers to the lake surface area of the year, the year close to the hydrological frequency of the evaluation year at the end of the 20 th century 80 years is preferred.
In a further embodiment, when the light irradiates on the plants, most of the light in the near infrared band is reflected by the plants, most of the light in the visible light band is absorbed by the plants, and the influence generated by the ground feature spectrum can be eliminated through linear or nonlinear combination of the near infrared and red band reflectivity, so that the water body is distinguished, and therefore, when the annual lake surface area is evaluated, the water body range is extracted by using a normalized vegetation index model. Wherein the corresponding normalized vegetation index calculation expression is:
in the formula, IR represents the reflectance in the near infrared band; r represents an infrared band reflectance. When the value of the NDVI is negative, the current water body is represented; otherwise, the vegetation soil is obtained, the NDVI value of the vegetation soil is relatively large, the overall histogram of the NDVI shows a bimodal distribution form, and the water body can be obtained through a threshold value mode.
And (3) acquiring a water body grid surface by adopting grid calculation based on data acquired by the NDVI calculation expression, and acquiring the water surface area of the lake in the evaluation year after vector conversion, thereby realizing the calculation of the shrinkage proportion of the lake area.
In the preferred embodiment, for the evaluation of the shrinkage ratio of the lake area, the monitoring frequency is 1 time/year. Compared with the prior art that the lake area is obtained by a visual interpretation method, the method has stronger subjectivity. The method provided by the embodiment is simple and easy to implement, is suitable for remote sensing data of various data sources, has strong universality and high operation efficiency, and achieves the purpose of improving the accuracy of index evaluation and the evaluation efficiency.
Example four
In a further embodiment based on the embodiment, the current river and lake appearance state is judged by carrying out image analysis on the remote sensing data, and the current river and lake health state is evaluated by comparing and analyzing with the historical condition.
Specifically, aiming at remote sensing data of a historical reference period and an evaluation period, firstly extracting the outline of an analysis region, and obtaining a test image of a target region after data preprocessing; and then, comparing the analyzed data with historical data to obtain change data, thereby realizing the evaluation of the appearance state.
In a further embodiment, the process of obtaining the test image of the target area specifically includes the following steps:
step 1, reading remote sensing data;
step 2, acquiring a format which accords with required image data through data preprocessing;
step 3, constructing a reflection matrix according to the image data format;
and 4, constructing a projection coordinate system, and generating a test image according to a preset wave band.
Specifically, after decompressing the original data downloaded by the high-resolution satellite, traversing the search file to find the file where the required information is located, and reading the required information from the file, such as the storage path of each band data, the resolution of each band data, the coordinate information of the upper left corner of the data, and the remote sensing image information such as NoData represented by countless points; integrating and acquiring required information, integrating the acquired upper left corner coordinates, the resolution in the east-west direction and the rotation angle information of the map to construct a reflection matrix required by the remote sensing image data in the Geotiff format, selecting a corresponding coordinate system as a projection coordinate system, selecting wave band data to generate a test image, and selecting red, green and blue wave bands to generate a remote sensing data RGB image; and finally, acquiring boundary information of the target area according to the shp file of the target area, acquiring the maximum and minimum longitude coordinates and the maximum and minimum latitude coordinates of the boundary of the target city, forming a rectangular frame according to the maximum and minimum longitude and latitude coordinates, cutting the test data and the RGB image according to the rectangular frame, and performing null whitening processing on data outside the boundary of the target area.
In a further embodiment, after the test image is obtained, semantic segmentation is performed on the test image, the trained first classification network model is used for testing the test image data of the target area, and then the image of the target area in which each pixel is converted into the corresponding class label is obtained.
Specifically, firstly, classifying objects in the remote sensing image, and labeling all pixels of the remote sensing image; secondly, slicing the whole remote sensing image to obtain pixel blocks; thirdly, dividing the obtained pixel blocks into a verification data set and a test set; and then, constructing a region segmentation model for segmenting the target region to obtain an image with a corresponding class label.
In the process of obtaining the pixel blocks through slicing, each pixel in the image is taken as a central point and is cut into pixel blocks with the size of m multiplied by c, wherein m is the side length of the pixel blocks, c is the wave band number of the pixel blocks, and c is larger than 3.
The constructed region segmentation model is a first classification network model and is based on a convolutional neural network structure, and the model can also be called a four-random region segmentation model based on pixels. The model structure is a 3D-2D convolution structure, and the whole network is formed by connecting 3 layers of 3-dimensional convolution layers, two layers of 2-dimensional convolution layers and 3 layers of full-connection layers in series. In a preferred embodiment, the convolution kernel sizes of the 3D convolutional layers may be (3 × 3 × 3), (3 × 3 × 5), (1 × 1 × 4), respectively, and the convolution kernel size of the 2D convolutional layer may be (3 × 3); the 3D convolutional layers are connected with the 2D convolutional layers through dimensionality reduction, and the 2D convolutional layers are connected with the full-connection layers through dimensionality reduction of the input to one dimension.
In a further embodiment, the method comprises the steps of selecting the regional outline of a test image after semantic segmentation, carrying out binarization processing on each category label in the image, selecting pixel points of which the category labels are bare land categories as target pixel points, setting the numerical values of the target pixel points as first numerical values, and setting the values of the rest pixel points as second numerical values; and (4) gathering the binarized category labels to regionalize adjacent and same-category pixel points, finding the difference between the inner pixel value and other pixel values according to the region, acquiring the coordinates of the points at the region boundary and forming a contour.
Specifically, when a second classification network model is used for carrying out region contour selection on a semantically segmented test image, the maximum value and the minimum value of the abscissa and the ordinate are found in points forming a contour to form a rectangular region, the coordinate of the center point of the rectangular region is found, a data block of h multiplied by w multiplied by 3 is intercepted by taking the point coordinate as the center, the data block is input into the second classification network model to judge whether the region corresponding to the data block is a four-random region, and if the region is not the four-random region, the contour corresponding to the data block is deleted.
In the preferred embodiment, the evaluation of the appearance state of the rivers and the lakes comprises four phenomena, namely random stacking, random occupation, random mining and random construction, the monitoring of the condition indexes of the 'four random' states of the rivers and the lakes needs to cover all the year round, dynamic monitoring or even real-time monitoring is needed, and the monitoring frequency is required to be higher. The existing monitoring method mainly depends on-site investigation and erection of sensing equipment, and has the advantages of small observation range and high labor cost. The analysis method in the embodiment is used for monitoring, so that the method has more advantages in space scale and monitoring frequency, the algorithm operation efficiency is high, and meanwhile, support can be provided for automatic monitoring and evaluation of river and lake health.
EXAMPLE five
In one embodiment, a river and lake health index evaluation system based on satellite remote sensing data is provided, and the system specifically comprises the following modules:
the data reading module is used for acquiring analysis data of the remote sensing indexes;
the model building module is used for building an index evaluation model;
and the evaluation module is used for evaluating the health condition of the rivers and the lakes.
In a further embodiment, firstly, based on a research period, reading remote sensing index analysis data in a database by using a data reading module; then, a model construction module is adopted to construct an index evaluation model, and read remote sensing index analysis data are received; and finally, based on the analysis result of the index evaluation model, evaluating the health condition of the rivers and the lakes by using an evaluation module.
EXAMPLE five
In one embodiment, a computer-readable storage medium is provided having computer program instructions stored thereon, the corresponding computer program instructions when executed by a processor to implement an index evaluation method.
As noted above, while the present invention has been shown and described with reference to certain preferred embodiments, it is not to be construed as limited thereto. Various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A river and lake health index evaluation method based on satellite remote sensing data is characterized by comprising the following steps:
step 1, constructing a satellite remote sensing data storage database;
step 2, reading remote sensing index analysis data in a satellite remote sensing data storage database within a preset time period and a space range through a data reading module;
step 3, constructing an index evaluation model by a model construction module, and receiving read remote sensing index analysis data;
and 4, analyzing data based on the remote sensing indexes read in the step 3, and evaluating the health conditions of the rivers and the lakes by an evaluation module.
2. The method for evaluating the river and lake health indexes based on the satellite remote sensing data according to claim 1, wherein in the process of comprehensively evaluating the health conditions of the river and the lake based on the analysis result of the index evaluation model, the method further comprises the following steps:
step 3.1, analyzing the vegetation coverage rate of the river and lake shoreline;
step 3.2, acquiring the area shrinkage proportion of the lake;
and 3.3, comparing the appearance conditions of the rivers and the lakes.
3. The method for evaluating the health index of the rivers and the lakes based on the satellite remote sensing data as claimed in claim 2, wherein when the vegetation coverage rate of the coastline of the rivers and the lakes is analyzed, the method specifically comprises the following steps:
step 3.1.1, segmenting the river and lake shoreline according to a preset distance;
step 3.1.2, calculating the vegetation coverage rate of each bank section according to the sections;
step 3.1.3, carrying out weight weighting calculation on the length of the opposite bank segment based on the whole bank line;
and 3.1.4, acquiring the vegetation coverage of the whole shoreline through weighted summation.
4. The method for evaluating the health index of the rivers and the lakes based on the satellite remote sensing data as claimed in claim 2, wherein when the shrinkage proportion of the lake area is obtained, the method specifically comprises the following steps:
step 3.2.1, obtaining historical data;
step 3.2.2, acquiring a water body range in the research area through the difference of illumination wave bands;
and 3.2.3, calculating the lake shrinkage proportion of the research area by adopting a proportion mode based on the historical data.
5. The method for evaluating the health index of the rivers and the lakes based on the satellite remote sensing data as claimed in claim 2, wherein when the appearance presenting conditions of the rivers and the lakes are compared, the method specifically comprises the following steps:
step 3.3.1, obtaining remote sensing image data of a research area;
step 3.3.2, preprocessing the remote sensing image data to obtain a test image;
3.3.3, constructing a segmentation model, and converting the test image of the target area into an image with a corresponding class label by using the segmentation model;
3.3.4, extracting the outline of the research area based on the converted image data;
and 3.3.5, analyzing the image characteristics in the research area, comparing the image characteristics with the historical characteristics of the research area, generating a final comparison result and outputting the final comparison result.
6. The method for evaluating the river and lake health indexes based on the satellite remote sensing data according to claim 5, wherein the process of acquiring the target area test image specifically comprises the following steps:
step 3.3.2.1, reading remote sensing data;
step 3.3.2.2, acquiring a format which accords with the required image data through data preprocessing;
step 3.3.2.3, constructing a reflection matrix according to the image data format;
and 3.3.2.4, constructing a projection coordinate system, and generating a test image according to a preset wave band.
7. The method for evaluating the health indexes of rivers and lakes based on satellite remote sensing data according to claim 5, wherein the process of converting the test image of the target area into the image with the corresponding class label by using the segmentation model specifically comprises the following steps:
step 3.3.3.1, obtaining a test image;
step 3.3.3.2, labeling each pixel in the test image;
3.3.3.3, slicing the test image according to a preset specification to obtain a pixel block;
step 3.3.3.4, dividing the pixel block into a verification data set and a test set according to the proportion;
step 3.3.3.5, constructing a segmentation model, and performing model training by using the verification data set and the test set;
and 3.3.3.6, converting the test image of the target area into an image corresponding to the class label by using the trained segmentation model.
8. The method for evaluating the river and lake health indexes based on the satellite remote sensing data according to claim 5, wherein the process of extracting the outline of the research area based on the converted image data specifically comprises the following steps:
step 3.3.4.1, carrying out binarization processing on various labels in the test image;
3.3.4.2, selecting pixel points corresponding to the labels as target pixel points according to requirements, defining the numerical value of the target pixel points as a first numerical value, and defining the residual pixel point value as a second numerical value;
step 3.3.4.3, grouping the binary class labels to regionalize adjacent and same-class pixel points;
and 3.3.4.4, obtaining the coordinates of points at the boundary of the area and forming a contour according to the difference between the pixel values in the area and the residual pixel values.
9. A river and lake health index evaluation system based on satellite remote sensing data is used for realizing the index evaluation method of any one of claims 1 to 8, and is characterized by specifically comprising the following modules:
the data reading module is used for acquiring analysis data of the remote sensing indexes;
the model building module is used for building an index evaluation model;
and the evaluation module is used for evaluating the health condition of the rivers and the lakes.
10. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the index evaluation method of any one of claims 1 to 8.
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