38 results about "Water index" patented technology

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

The invention discloses a coastline characteristic change analysis method based on a remote sensing image, and relates to the technical field of coastline monitoring. Comprising the following steps: collecting Landsat TM / OLI data, preprocessing the data, carrying out sea-land classification and water body index extraction on a remote sensing image of a research area by adopting an object-oriented nearest neighbor algorithm, carrying out post-processing such as vector data conversion and shoreline smoothing processing on final shoreline information to obtain a shoreline data set, and carrying out spatial-temporal change analysis. And by combining a plurality of remote sensing image processing technologies and a statistical analysis method, the coastline feature change can be accurately extracted and analyzed, and the influence of human factors and natural environment in a traditional method is avoided.

The invention provides an adaptive optimal water body index threshold determination method and system based on a convolutional neural network, a remote sensing image water surface extraction method, a medium and equipment, and belongs to the technical field of water color remote sensing. According to the adaptive optimal water body index threshold value determination method, reflectivity data of a green light wave band and a near infrared wave band and NDWI statistical information are taken as input, a convolutional neural network is constructed and trained, and an adaptive optimal NDWI index threshold value is determined. The system comprises a preprocessing module, an operation module, a statistics module and a model construction and training module. The water surface extraction method is implemented according to an adaptive optimal NDWI index threshold. According to the method, only the green light wave band reflectivity data, the near-infrared wave band reflectivity data and the NDWI statistical information need to be input, the single self-adaptive optimal NDWI index threshold value is output, planar water body and non-water body information does not need to be output, a training occupation video memory does not need to be added, and computing resource consumption and training difficulty are effectively reduced.

The invention provides a mining area surface water area mining influence evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining remote sensing images of a target mining area before and after mining, and carrying out the spectral feature enhancement of the remote sensing images to obtain a normalized water body index; performing rough identification on the surface water area based on the normalized water body index to obtain a suspected water body area; performing fine identification on the suspected water body area to obtain a high-precision surface water area classification result; performing post-processing on the high-precision surface water area classification result; according to the total area of the surface water area before mining and the total area of the surface water area after mining, indexes of the surface water area influenced by mining of the target mining area are calculated, a quantitative evaluation result is output, the spatial integrity and boundary precision of water body recognition are improved, and the defects that a traditional remote sensing method is prone to noise interference and the extraction result is rough are effectively overcome; the evaluation method which is low in cost, automatic, quantitative and high in safety is realized, and powerful decision support is provided for protection of the surface water area of the mining area.

The present invention discloses a method for calculating pond water storage capacity based on dynamic grid sampling, which relates to the field of water resource monitoring technology. The method comprises: extracting pond water surface based on optical images and an improved normalized water index, calculating the pond area and number within a mission area; and using a grid sampling method to classify and sample the ponds; calculating the pond density and grid width based on the land area, pond area, and number within the mission area, dynamically generating a grid, and batch sampling the ponds using programming software; using big data analysis based on urban land space monitoring road network data, evaluating the quality of pond samples and replacing them, encoding the pond samples, and constructing a sample database using geographic information software; and constructing a water depth inversion model using programming software and a machine learning algorithm to obtain water depth inversion results. The water storage capacity of the ponds is calculated based on the pond area. The present invention can optimize the spatial coverage balance of samples.

The invention relates to the technical field of data processing, and particularly discloses a method for evaluating mine environment health by using leaching water indexes, which comprises the following steps: acquiring a water quality index sequence after weighted fusion of the pH value and the heavy metal content of mine leaching water; determining a trend residual sequence corresponding to the potential risk interval section; a trend residual error in the trend residual error sequence is fused with the average seepage velocity of the current mine area to generate a comprehensive ecological balance vector; extracting a trend residual error sequence corresponding to the high-risk classification label, and taking the trend of the sequence in a future rated time window as a trend feature prediction sequence; according to trend residual errors in the trend feature prediction sequence, an environmental health condition score value is obtained through calculation; and obtaining a grading result of the current area based on the environmental health condition score value, and forming a processing strategy according to the grading result. According to the method for evaluating the health of the mine environment by using the leaching water index, the problem that the health condition of the mine environment is not accurately evaluated at present is solved.

This invention relates to a method for estimating the chemical oxygen demand (COD) storage of reservoirs over a long time series. The method includes: identifying the reservoir surface area using remote sensing data combined with a normalized water index and a threshold; performing Census X-11 time series decomposition on the identified reservoir surface area to obtain the contribution ratios of seasonal, trend, and irregular components; conducting a Mann-Kendall test on the trend components to determine the year corresponding to the abrupt change in reservoir area; obtaining the average COD concentration of the reservoir over a time series and establishing a correlation fitting model with the year; using the correlation fitting model and the year to estimate the average COD concentration of the reservoir in different years, and multiplying it by the total reservoir capacity to obtain the COD storage of the reservoir in different years. This invention is the first to construct a method for estimating the annual average COD concentration of reservoirs, achieving remote sensing estimation of the total COD storage of reservoirs in different years.

The present invention relates to an automatic tidal creek extraction method based on color space component water index replacement and fusion, comprising the following steps: acquiring a target remote sensing image; calculating the normalized water index NDWI, the improved normalized water index MNDWI, and the multispectral water index MuWI; performing color synthesis to obtain a color image; performing HIS transformation, using the obtained I component as a new water index NWI; performing gray-level co-occurrence matrix (GLCM) calculation on the NWI image to obtain average features; processing the average features through an optimized top-hat transformation; statistically determining an optimal threshold for extracting tidal creeks to generate a binary image; and performing connected component analysis and morphological operations on the binary image to obtain the final result of tidal creek extraction. The present invention has the beneficial effects of reducing the impact of background heterogeneity on tidal creek extraction, effectively enhancing the linear features of tidal creeks, better distinguishing tidal creeks from background, and improving the accuracy of tidal creek extraction.

The present application provides a kind of based on environmental parameter adaptive coastline intelligent extraction method and system, method includes: obtaining the remote sensing image data of target area of the coastline to be extracted and its imaging phase tidal level data, and the remote sensing image data is preprocessed;Based on the remote sensing image data after preprocessing, the SSC of target area is calculated by inversion, and the corresponding adaptive water index graph of target area is dynamically selected by water index;Adaptive water index graph is threshold segmented, and preliminary water mask graph is obtained;According to tidal level data, the tidal period of imaging phase is judged, and based on tidal period, preliminary water mask graph is dynamically selected by morphological operation and boundary optimization is carried out, and the water mask graph after optimization is obtained;From the water mask graph after optimization, coastline is extracted and postprocessed, and the final target area coastline is obtained;The present application can realize the high-precision, high-robustness coastline automatic extraction under the complex nearshore dynamic change environment.

The invention relates to the technical field of image processing, in particular to a large-range water area time-space dynamic change monitoring method fusing remote sensing and GIS, and the method comprises the steps: obtaining the hyperspectral remote sensing image data of a to-be-monitored water area in real time; aiming at each moment, constructing a feature vector of each pixel point and a center vector of each area label in the water area to be monitored by combining a normalized water index and a normalized vegetation index of each pixel point through gray difference and texture feature difference between each pixel point and an adjacent pixel point in the hyperspectral remote sensing image data and by combining the normalized water index and the normalized vegetation index of each pixel point; and constructing an energy function of a graph cut algorithm for segmenting the to-be-monitored water area, segmenting the to-be-monitored water area at each moment through the energy function, and carrying out spatial-temporal dynamic change monitoring on the to-be-monitored water area through segmentation results at different moments. The invention aims to improve the accuracy of monitoring the spatial-temporal dynamic change of the water area by improving the accuracy of area segmentation.

As water body signals are generally less than 5%, this paper proposes a multispectral remote sensing random optimal ratio logarithmic water depth inversion method. It comprehensively considers the influence of solar spectrum, total upward scattering rate, solar zenith angle, global total transmittance, vertical irradiance and total spherical albedo, constructs an atmospheric coupled radiation calibration correction model to correct the atmospheric and water surface reflectivity, and uses a quotient interpolation tide height correction model to correct the tide height. The FNDWI normalized difference water index accurately extracts water bodies, and the random optimal model effectively extracts sub-datasets. The extreme gradient boosting method optimizes model parameters to obtain the optimal ratio logarithmic model for water depth inversion. Compared with the water depth inversion of the Stumpf model, the R of the water depth inversion of this model is better than that of the Stumpf model. 2 The accuracy is improved by 0.592 and the RMSE is improved by 0.841m.

The invention relates to a remote sensing image quality evaluation method for an island shallow water area. The method comprises the following steps: separating an island underwater area from a land area by adopting a normalized water body index; extracting the shallow water area of the island reef by adopting relative water transparency and suspended solid total turbidity; constructing a cloud index, a cloud shadow index and a flare index to evaluate the cloud, shadow and flare conditions of the island shallow water region, and finally obtaining a plurality of special indexes; and 4, evaluating the image quality of the shallow water region of the island reef by using a special index and a general index (standard deviation, information entropy and definition). A remote sensing physical model and a traditional image quality evaluation method are organically combined, and the problem that an existing method is insufficient in applicability in an island shallow water area is effectively solved. By establishing a targeted quality evaluation system, accurate evaluation of an effective shallow water area in a satellite image is realized, and an automatic and efficient image quality evaluation solution is provided for island remote sensing monitoring.

The invention relates to the field of remote sensing science, and discloses a layered water body extraction method for enhancing water body index driving, which comprises the following steps: preprocessing a remote sensing image; calculating a remote sensing index; segmenting ground feature features; performing hierarchical feature fusion and decision voting; and water body space distribution and drawing. According to the method, the enhanced water body index EWI is constructed by using the space-time invariance of ground feature spectral information, and the ground feature features in the scene are subjected to conjoint analysis in combination with remote sensing indexes such as vegetation and cloud, so that accurate quantitative recognition of surface water body distribution is realized. Aiming at the problem that a water body in a remote sensing image and other interference ground objects present a similar trend in spectral characteristics, the method is combined with EWI and other remote sensing indexes to provide a layered water body extraction strategy, so that a spectral signal of the water body can be remarkably enhanced under a thin cloud coverage condition, interference caused by cloud and shadow can be effectively inhibited, and the accuracy of the water body extraction is improved. The accuracy of water body extraction is improved; the method has wide application prospects in the fields of flood disaster emergency response, water resource management and the like.

The application discloses a kind of ground water level monitoring methods based on multispectral satellite, it is characterized in that, propose a kind of special water gauge, special water gauge is by red, white grid interval distribution composition, combined satellite remote sensing technology and projection amplification method, using near infrared, green band calculation and classification normalized water index, or combined near infrared, infrared and red band to identify water body.Under the satellite image resolution D2×D2, grid D1×D1 on water gauge can be identified and read, water gauge reading is amplified (H / L) times, water level in water tank is obtained by conversion, and then water area water level is obtained.The application can intuitively identify water body, read water gauge reading, convert the identification of satellite image to the identification of vertical size, and make the water level accuracy meet the use requirements, finally achieve the purpose of satellite intuitive monitoring water level.The application method is simple and effective, and has strong adaptability, and it is very easy to use satellite image to intuitively obtain water level data meeting accuracy requirements.

A method, system, and device for water segmentation in remote sensing images using multi-path fusion of water index and polarization information utilizes two types of multi-source remote sensing data, multispectral images and SAR images, introduces a water index into the original UNet algorithm, designs a multi-path and multi-source data fusion encoder, and utilizes a Gated Channel Transform (GCT) attention mechanism to address the adverse effects of complex land environments in remote sensing images on water segmentation, thereby improving the accuracy of water segmentation with fewer computing resources. The system and device are used to implement water segmentation in remote sensing images using multi-path fusion of water index and polarization information. The present invention has the advantages of low computing cost, short detection time, and high segmentation accuracy, and can be iteratively updated at any time to improve model performance.

The present invention discloses a method for removing mountain shadows from SAR images using a dynamic threshold based on an inverse exponential function, which relates to the technical field of remote sensing image data processing. The method comprises the following steps: preliminarily extracting water areas based on SAR images using the dual-polarization water index (SDWI), and randomly generating sample points within the water area; assigning corresponding elevation and slope attributes to each sample point using digital elevation data (SRTM), and then manually interpreting the optical image to obtain a sample data set containing two types of sample points: mountain shadows and water bodies; removing outliers from the sample data set using the interquartile range method, performing a thinning process on the sample points to calculate the dividing point between the two types of sample points, and constructing an inverse exponential dividing function model for distinguishing mountain shadows from water bodies. Therefore, the above-mentioned method for removing mountain shadows from SAR images using a dynamic threshold based on an inverse exponential function effectively eliminates false water areas misjudged due to terrain shadows, thereby improving the authenticity and consistency of the spatial distribution of water bodies.

The invention discloses a complex terrain water body extraction method based on an improved normalized difference water body index, and the method comprises the steps: analyzing the spectral difference of a water body and an interference ground feature in a Landsat 8 multi-band image under a complex terrain, and constructing a dynamic inhibition coefficient to optimize a traditional water body index; according to the dynamic inhibition, r1 eliminates high-reflection buildings through a short wave infrared band, r2 eliminates low-reflection mountain shadows based on a green light band, and r3 inhibits ice and snow interference according to the visible light-near infrared average reflectivity. According to the self-adaptive extraction, the inhibition coefficient and the NDWI are combined to generate an RNDWI index, accurate classification of the water body is achieved through threshold segmentation, and finally multi-scene interference is effectively filtered out while the real water body is reserved. According to the invention, the overall precision reaches 98.3% and is improved by 7.5% compared with NDWI; the building misjudgment rate is reduced by 92%; the mountain shadow extraction missing rate is reduced by 85%; the ice and snow mixing rate is close to 0; and multi-scene superposition is supported, and terrain self-adaption is realized through coefficient dynamic combination.

The invention relates to a small water body recognition method based on satellite remote sensing image super-resolution reconstruction, and the method comprises the steps: constructing a pre-sampling diffusion probability super-resolution reconstruction model ED-DDIM of a series pre-sampling super-resolution network pre-EDSR and an improved DDIM diffusion probability model, and introducing a multi-channel image including an NDWI water body index channel for a remote sensing image, a super-resolution reconstruction model is obtained, details in an image can be effectively recovered, the distortion problem is reduced, gradual reconstruction from a low-resolution image to a high-resolution image is achieved through a diffusion iteration denoising mechanism in combination with the multi-scale feature extraction capability of UNet jump connection, and then a pre-trained water body recognition model is applied to improve the recognition accuracy of the water body. A binary classification image of a water body area and a non-water body area of a remote sensing image restored by a multi-channel image under high resolution is obtained, water body area identification is realized, and contributions are made to monitoring in an increasingly changing environment and arid area water resource protection.

The invention discloses an extract water content detection method and system based on a two-waveband hyperspectrum, and belongs to the field of extract production. In order to realize efficient, lossless, real-time and accurate detection of the water content of the extract, the method for detecting the water content of the extract based on the two-waveband hyperspectrum comprises the following steps: acquiring a hyperspectral image of the extract in drying treatment; according to the spectral images of at least two different wavelengths, at least obtaining reflectivity corresponding to the two different wavelengths; obtaining the moisture index of the extract at least according to the reflectivity corresponding to the two different wavelengths; and calculating the dehydration rate of the extract according to the water index of the extract. The method can be applied to an extract production system, and has the advantages of high efficiency and real-time detection of the water content of the extract.

This paper proposes a water body identification and extraction method and system based on bilateral red band GF6-WFV data, including: preprocessing GF6 satellite WFV image data to obtain atmospherically corrected multi-angle observation data for each satellite band; calculating the reflectivity of each satellite band, normalized to the same zenith angle after atmospheric correction, to obtain the angularly normalized reflectivity of each band; superimposing bands with high and low reflectivity effects and then normalizing them to eliminate the noise impact of areas including shadows and dark surfaces; calculating a targeted high-resolution water index and the normalized difference vegetation index (NDVI). This paper reveals the differences in sensitivity of different bands in water body monitoring and can effectively distinguish water bodies from other land cover types in complex natural and urban environments, making the water body identification process more accurate.

The invention provides a drainage basin hydrological toughness evaluation and regulation method based on a Budyko framework, and belongs to the technical field of hydrological water resource management and environment monitoring. Firstly, an improved Budyko framework and a standardized rainfall index (SPI) are adopted to carry out hydrological toughness analysis calculation and mutation analysis research on the Weihe river basin; then, the driving force influencing the hydrological toughness of the watershed and the regulation and control mechanism of the driving force are analyzed by adopting CCM-Geoderator; and finally, constructing a natural scene without human activities through a VIC model, and illuminating the influence of human climate change on hydrological toughness. The new hydrological toughness evaluation method provided by the invention can comprehensively explore the space-time evolution characteristics and driving force of the hydrological toughness in the Weihe River basin, and provides effective support for improving the ecological restorability of the basin and promoting sustainable development.

The invention provides a bilateral red wave band-based GF6-WFV data water body identification and extraction method and system, and the method comprises the steps: preprocessing GF6 satellite WFV image data, and obtaining multi-angle observation data, subjected to atmospheric correction, of each wave band of a satellite; performing atmospheric correction, unifying to the same zenith angle, calculating the reflectivity of each wave band of the satellite, and obtaining the normalized reflectivity of each wave band angle; respectively superposing the wave band with the high reflection effect and the wave band with the low reflection effect and then normalizing, eliminating the noise influence of the area comprising the shadow and dark surface, calculating the targeted high-water-diversion index, and calculating the normalized vegetation index NDVI. According to the method, the sensitive difference of different wave bands in water body monitoring is revealed, the water body and other land cover types can be effectively distinguished in complex natural and urban environments, and the water body recognition process is more accurate.

The present application relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a kind of black and odorous water intelligent identification method based on multi-source remote sensing image, the method first obtains first high-resolution remote sensing image, extracts global water mask using TransUNet model;Second, based on geometric features and basic spectral rules, non-target patches are removed;Then, the black and odorous water index is calculated using the second multispectral remote sensing image, and the suspected black and odorous water is screened by combining adaptive threshold segmentation;Further, the suspected target is finely classified and confirmed using the third high-frequency remote sensing image and Efficient Net model;Finally, the identification results are fused and output.The present application overcomes the limitations of insufficient spatial and temporal resolution of a single data source through multi-source data collaboration and coarse screening-fine classification multi-level strategy, improving the positioning accuracy and anti-interference ability of urban black and odorous water identification.

The invention discloses a joint scheduling method for a branch leading project based on a water storage index, belongs to the technical field of water supply scheduling, and can solve the problem of low project operation efficiency caused by incoordination of supply and storage modes in the prior art. The method comprises the following steps: S1, according to hydrological information of a main stream and water demand information of a water using object, dividing a scheduling period into a first scheduling period in which the water storage amount is greater than the water supplementing amount and a second scheduling period in which the water storage amount is smaller than the water supplementing amount; s2, determining a plurality of correlation factors of the water storage indexes, and determining the water storage indexes of the first scheduling time period and the second scheduling time period according to the plurality of correlation factors; s3, constructing a combined scheduling model of the water supply reservoir and the water storage and storage reservoir according to the water storage indexes of the first scheduling time period and the second scheduling time period; and S4, determining a combined scheduling scheme of the water supply reservoir and the water storage and storage reservoir by using the combined scheduling model. The method is used for carrying out joint scheduling on the lead-in branch project.

The invention provides a crop whole-growth-period drought monitoring and early warning method based on an adaptive water shortage index. The method comprises the following specific steps: establishing a regression relationship between a crop coefficient and a leaf area index; calculating the daily potential evapotranspiration; establishing an effective rainfall experience calculation formula, and calculating day-by-day effective rainfall according to the rainfall observed by the meteorological station; constructing adaptive day-by-day-scale crop water shortage indexes considering different crop types and drought-enduring characteristics in different growth stages; setting a crop drought grade division standard based on the adaptive water shortage index, carrying out crop drought identification and grade research and judgment, and achieving the monitoring and early warning of the whole process of crop drought occurrence, development and ending. According to the method, multi-source data such as remote sensing data, meteorological data and crop characteristic data are fused, the problem of water shortage sensitivity difference of different crop types and different growth stages is considered, the adaptive day-by-day scale crop water shortage index is constructed, and the whole drought process of the crop growth stage can be monitored.

The application provides an environmental ecological restoration monitoring device and method, which collects microwave radiation data of a bare ground surface at a sampling point in a desert restoration area from multiple sampling angles through microwave remote sensing; determines an escape coefficient of microwave at each sampling angle according to the roughness and dielectric constant of the soil at the sampling point; determines a linear polarization angle of the soil at the sampling point according to the correlation between all the escape coefficients and all the sampling angles, and then identifies the polarization loss of the microwave remote sensing at the sampling point based on the linear polarization angle; determines a water retention index of the soil at the sampling point according to the soil temperature and the polarization loss at the sampling point; sets other sampling points in the desert restoration area, continues to determine the water retention index of the soil at the other sampling points, and monitors the restoration effect of the desert restoration area according to all the water retention indexes. The application can reduce the error caused by microwave polarization in the process of detecting the water content through microwave remote sensing.

The invention provides a coastal aquaculture classification method based on spectrum time sequence characteristics, and the method comprises the steps: obtaining the time sequence data of a remote sensing image of a target region, and calculating a plurality of spectral indexes at least comprising a normalized algae index NDAI and a normalized aquatic water index NDAWI; sequentially carrying out time resampling, missing data filling by linear interpolation and Savitzky-Golay filtering processing on the spectral index, and constructing a smooth time sequence feature set; based on the smooth time sequence feature set, respectively constructing a single-index time sequence feature and a multi-index fusion time sequence feature; inputting the single-index time sequence features and the multi-index fusion time sequence features into a plurality of machine learning models for training, and selecting an optimal model-feature combination through precision comparison; and classifying the target area by using the optimal model-feature combination, and outputting an aquaculture classification result.

The present invention discloses a water index prediction method based on sewage treatment. The method first stirs and settles the water, then divides the area and depth and establishes a local water supply model and performs a first adjustment on the water inlet index. Then, a logarithmic function model is established to predict the water inlet index at different depths. Finally, five models are established to respectively predict the pH value, COD, ammonia nitrogen content, total phosphorus content, and total nitrogen content, and the predicted values ​​are displayed after corresponding to time. This application first supplies water after standing still and models it in layers, and then combines the vertical distribution characteristics of the fluid after standing still to accurately reflect the dynamic changes of water quality indicators. Based on the law that the concentration of pollutants decays with time during precipitation and diffusion, a logarithmic function is constructed to predict the water inlet index in the depth direction to reduce the number of sampling times. Through grid dynamic correction, the arithmetic mean is used to weight the drainage volume ratio, which reduces the sampling deviation caused by flow disturbance and improves the robustness of the prediction. The above scheme can ensure that the sewage quality is relatively uniform, and then the drainage volume ratio is weighted by the average value of the water inlet index of each area to obtain the optimized water inlet index. The optimized water inlet index is then calculated through the constructed logarithmic function to obtain the water inlet index at different depths, and the water inlet index at different depths is input into the five groups of models set for calculation and prediction to obtain the water outlet index.

The invention discloses a response analysis method, device, equipment and medium for drought stress to forest fire natural causes, and relates to the field of forest fire cause analysis, and the method comprises the following steps: determining drought stages and annual day ranges corresponding to the drought stages according to precipitation of a forest fire area for many years in history; acquiring a multi-time-sequence remote sensing surface reflectance image in each year day range in different drought stages of each year in history; synthesizing the multi-time-sequence remote sensing surface reflectance images based on the surface water index to obtain a surface water index image; determining a vegetation drought stress trend and a change rate according to the surface water index image; and determining the area proportion of the drought stress forest land in the forest fire area according to the vegetation drought stress trend and the change rate so as to determine the contribution degree of the drought stress to the natural cause of the forest fire. According to the method, the natural cause influence of the drought stress on the forest fire can be accurately quantified, and the scientificity and practicability of analyzing the natural cause influence of the drought stress on the forest fire are improved.

The application discloses a remote sensing image water body and shadow classification method based on a double classification attention network, relates to the technical field of remote sensing image analysis and geographic information processing, and comprises the following steps: preprocessing a Sentinel-2 original image to generate a multi-band composite image with a predetermined resolution; calculating a normalized water index and extracting a brightness channel to respectively generate a water body mask and a shadow mask, constructing a sample set, and dividing the sample set into a training set and a verification set; constructing a double classification attention network model based on the training set, and optimizing and training the model; using the trained double classification attention network model to perform sliding window reasoning on the multi-band composite image, fusing and splicing the predicted binary mask, and outputting a double-band water body / shadow binary mask image. The application can effectively overcome the interference of ice and snow shadow areas on the water body recognition result, and has better robustness and stability.