95 results about "Topographic correction" patented technology

The invention relates to the technical field of growth state evaluation, and discloses a crop growth state evaluation method and system based on multi-dimensional monitoring. The method comprises the steps of collecting multi-source remote sensing data of a farmland area according to a crop growth period, and performing topographic correction on the multi-source remote sensing data to obtain target vegetation data; based on the multi-source remote sensing data, farmland plot boundaries are extracted, and a farmland space association graph is constructed; inputting the target vegetation data and the farmland space association graph into an elevation perception graph convolutional network for elevation feature analysis, and calculating to obtain a crop abnormal growth index; and generating a growth state evaluation result based on the target vegetation data and the crop abnormal growth index. According to the method, crop growth abnormity caused by regional factors can be accurately identified, so that the accuracy of evaluation results under different terrain and environmental conditions is ensured.

The invention discloses a mountainous area accumulated snow area reconstruction method based on machine learning. The method comprises the steps that optical remote sensing data and topographic data are acquired and preprocessed; the optical remote sensing data comprises the MOD09GA data and the Landsat8OLI data; the preprocessing comprises terrain correction and accumulated snow recognition; taking an accumulated snow area generated by the Landsat8OLI data by using an accumulated snow recognition algorithm as a truth value label, and constructing a sample data set based on the preprocessed MOD09GA data and topographic data; dividing the sample data set into a training set and a verification set according to a proportion of 4: 1 so as to train and verify an XGBoost model; the XGBoost model improves the prediction performance by combining a plurality of decision trees; and performing accumulated snow area prediction on new input data by using the trained XGBoost model. According to the method, day-by-day 30-meter (m) accumulated snow area data can be produced, and the method has important research significance and value for mountainous area accumulated snow monitoring, hydrological process simulation and ecological system evolution.

The invention discloses a terrain and time sequence-based power transmission line icing risk prediction method, system and device, and a medium. The prediction method comprises the following steps: acquiring meteorological elements, topographic features and time sequence features, unifying the meteorological elements, the topographic features and the time sequence features to the same time step length and geographic coordinate grid, and performing topographic correction on the meteorological elements according to the topographic features; constructing and optimizing an icing risk prediction model based on the meteorological elements after terrain correction and the time sequence features as input; inputting real-time weather forecast data into the optimized icing risk prediction model, dividing icing risk grades according to model output, and performing spatial visualization and early warning through a GIS platform; and quantizing the contribution of each element to a prediction result, optimizing model parameters and a feature extraction method, and carrying out interpretable driven model iteration. According to the invention, refined prediction and dynamic early warning of the icing risk are realized, accurate operation and maintenance of a power system are effectively supported, and the icing disaster risk is reduced.

The invention discloses a remote-sensing image terrain correction optimization method based on deep learning, and the method comprises the steps: carrying out the preprocessing of a remote-sensing image sample, inputting the preprocessed remote-sensing image sample and an SE correction image sample into an initial terrain correction model, outputting a terrain correction image sample, and obtaining a terrain correction model; the initial terrain correction model is constructed by a codec and a discriminator; respectively calculating a pixel-level loss function value and a terrain consistency loss function value according to the terrain correction image sample and the SE correction image sample, and calculating an antagonism loss function value through a discriminator; and optimizing the initial terrain correction model based on the adversarial loss function value, the pixel-level loss function value and the terrain consistency loss function value, so that the optimized terrain correction model performs terrain correction on the to-be-processed remote sensing image. The method does not need to depend on specific parameters of the image, automatically extracts the image features only through the terrain correction model of deep learning, and achieves efficient terrain correction based on the image features.

The invention belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transfer model and machine learning, and the method comprises the following steps: obtaining and processing remote sensing data, DEM data and actually measured biomass data of a target region; performing terrain correction by adopting an SCS + C method; performing global sensitivity analysis on input parameters in the PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing topographic factors into the model, and correcting the solar zenith angle and the observation zenith angle; generating simulated canopy reflectivity data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectivity data and the above-ground biomass data, and performing machine learning modeling according to the canopy spectral data set; and verifying the inversion precision, and outputting a grassland above-ground biomass spatial distribution map. According to the inversion method, terrain factors are introduced on the basis of a traditional model, and the adaptability and estimation precision of the model to a complex terrain area are improved by combining a machine learning method.

The invention discloses a deep learning-based multi-source remote sensing image water body identification method and system, and the method comprises the steps: carrying out the topographic correction of a water body radar image, obtaining a water body radar correction image, carrying out the image registration and image division of the water body radar correction image and a water body optical image, and obtaining a water body optimization image; respectively inputting the water body optimization image into a double-branch feature extraction model and a depth feature extraction model to obtain water body characterization features and water body depth features, carrying out time sequence feature fusion on the water body characterization features and the water body depth features to obtain water body multi-source remote sensing features, and constructing a water body recognition model, and inputting a to-be-recognized water area water body multi-source remote sensing image into the water body recognition model to obtain a water body recognition result. The method not only can improve the efficiency and accuracy of multi-source remote sensing image water body recognition, but also has good interpretability, and can be directly applied to a multi-source remote sensing image water body recognition system.

The invention relates to a terrain correction method considering influence of multiple time phases and complex terrain, and the method comprises the steps: obtaining original remote sensing images and DEM data of a target terrain region under multiple time phases, and carrying out the preprocessing of the original remote sensing images and DEM data, and obtaining the preprocessed original remote sensing images and DEM data; performing terrain correction on the preprocessed original remote sensing image based on the preprocessed DEM data to obtain a reference image; inputting the preprocessed original remote sensing image and the corresponding DEM data into the initial terrain correction model, and performing iterative training based on a reference image corresponding to the original remote sensing image to obtain a target terrain correction model; the initial terrain correction model is constructed based on a generative adversarial network; and obtaining a to-be-corrected remote sensing image and the corresponding DEM data, performing preprocessing, and inputting the preprocessed to-be-corrected image and the corresponding DEM data into the target terrain correction model to obtain a target remote sensing image. The terrain correction effect and the processing efficiency of the remote sensing image can be improved.

The invention relates to the technical field of accumulated snow remote sensing modeling, in particular to an accumulated snow black carbon concentration remote sensing inversion method. Acquiring remote sensing topographic data of the target area and performing topographic correction; the reflectivity of a snow area in the remote sensing topographic data after topographic correction is obtained; establishing a pure snow optical model by adopting an analytic asymptotic radiation transfer theory according to the reflectivity of the accumulated snow area; introducing an ice-black carbon composite particle absorption characteristic into the pure snow optical model, constructing a dirty snow optical model, and performing optimization solution on the dirty snow optical model by adopting a strict radiation transmission model fitting result; and according to the optimized dirty snow optical model, carrying out inversion on the black carbon concentration of the accumulated snow area. According to the snow remote sensing inversion method provided by the invention, the influence of terrain shadow can be effectively reduced, the black carbon absorption coefficient is modeled into a wavelength function based on the snow radiation transmission model, the calculation complexity is greatly reduced, and the calculation efficiency and precision are improved.

The invention discloses an underlying surface information fusion accumulated snow depth inversion method and system based on an SAR image, and relates to the technical field of ice and snow monitoring based on a satellite remote sensing technology, and the method comprises the steps: collecting orbit data, carrying out the preprocessing of the collected orbit data, removing thermal noise and speckle noise, and carrying out the terrain correction. Performing radiation calibration according to a radar equation to obtain a back scattering characteristic parameter and a polarization characteristic parameter, performing characteristic extraction on the corrected data, optimizing a characteristic data set, constructing a neural network model, inputting the optimized characteristic data set into a residual neural network, training to obtain an accumulated snow depth inversion model, and introducing a time variable to obtain an accumulated snow depth inversion model; and predicting the snow depth. According to the method, the advanced SAR data processing technology and the machine learning method are fused, the snow depth inversion and prediction problems are effectively solved, and an important basis is provided for decision support.

The invention discloses an arid region identification method based on a GRACE satellite, and the method comprises the steps: obtaining satellite remote sensing original data, carrying out the median filtering of the satellite remote sensing original data, and obtaining noise reduction data; complementing missing data for the noise reduction data to obtain complemented data; calculating a normalized vegetation index and a temperature vegetation drought index according to the complementation data to obtain a drought index; time sequence features are extracted according to the drought indexes, principal component analysis is carried out on the time sequence features, and comprehensive indexes are obtained; spatial clustering analysis is carried out on the comprehensive indexes, and target areas with similar drought characteristics are divided; calculating a topographic relief degree and a land utilization type proportion based on the target area in combination with the land utilization data and the topographic data; selecting a target area of which the topographic relief degree is greater than a preset threshold value, and performing topographic correction on the drought index of the selected target area to obtain a corrected drought index; and judging the drought grade of the target area according to the corrected drought index and the land utilization type ratio.

The invention relates to the technical field of data processing, and discloses an accumulated snow depth detection method and system based on satellite navigation signals. The method comprises the steps that GNSS multi-band signals are collected through a multi-angle fusion antenna array, and direct and reflected signal components are obtained; performing phase tracking on the signal component to obtain an instantaneous phase difference sequence and a Doppler compensation parameter; analyzing the phase difference sequence by using an improved Lomb-Scargle algorithm, and extracting accumulated snow characteristic frequency and amplitude parameters; establishing a double-layer medium reflection model based on the characteristic parameters and performing terrain correction to obtain a theoretical phase difference curve; and through a multi-satellite collaborative inversion algorithm matching theory and an actually measured phase difference, outputting an accumulated snow depth measurement value. According to the invention, the problems of difficult multipath signal separation, poor terrain adaptability and unstable inversion precision in the existing GNSS reflected signal accumulated snow detection technology are solved, and the accuracy and reliability of accumulated snow depth detection under complex terrain conditions are improved.

The embodiment of the invention provides a ground distance geometric imaging grid division method and system for back projection of a synthetic aperture radar, and belongs to the technical field of radars. According to the scheme, satellite orbit parameters, radar parameters and radar echo data are acquired; center coordinate information of a scene center point is determined according to the satellite orbit parameters and the radar parameters, and the scene center point represents a geometric center point of the imaging area; dividing grids on the three-dimensional ground distance plane according to the central coordinate information and the imaging resolution by combining a digital elevation model to obtain a ground distance geometric imaging grid; and performing backward projection imaging processing on the ground distance geometric imaging grid according to the radar echo data to obtain an imaging result. According to the method, the process of terrain correction after conversion from a slant distance imaging plane to a ground distance imaging plane is not needed, the processing efficiency can be improved, and the synthetic aperture radar image with geocoding can be directly generated.

The invention relates to the technical field of photovoltaic power generation system optimization, in particular to a multi-objective collaborative optimization photovoltaic dip angle determination method, which comprises the following steps of: calculating solar radiation intensity received by a photovoltaic module based on a sun trajectory algorithm by collecting resource data, engineering parameters, terrain correction parameters and economic parameters; the assembly spacing and the land utilization rate are optimized in combination with full-time shadow analysis, and the minimum value of the occupied area is determined; a dynamic weight distribution strategy is adopted, factors such as power generation capacity, land rent and operation cost are coupled to calculate annual income, and the optimal photovoltaic dip angle is solved through a segmented iterative algorithm with the purpose of maximizing the total investment internal return rate (IRR). According to the method, collaborative optimization of land resource utilization and economic benefits can be realized, and a scientific dip angle decision basis is provided for photovoltaic projects with different land costs and topographic conditions.

The invention provides a remote sensing estimation method for the ablation rate of the bottom of an ice shelf, and the method comprises the steps: calculating the time difference between the data timestamp of ICESat-2 ATL15 and the data reference time of ATL14, multiplying the time difference by ATL15 to obtain the elevation change in the corresponding time, obtaining the elevation of the ice shelf of the corresponding timestamp based on ATL14, and taking the obtained elevation of the ice shelf as the reference to estimate the ablation rate of the bottom of the ice shelf. Repeating the steps to obtain ice shelf elevations of different timestamps; carrying out average dynamic terrain correction and ellipsoid elevation conversion on the elevation of the ice shelf; correcting the influence of the air content of the snow on the elevation change of the ice shelf; obtaining the thickness of the ice shelf through a static balance method; based on the principle of mass conservation, the thickness change caused by surface mass balance and rapid movement of the ice shelf is subtracted from the total thickness change of the ice shelf, then the thickness change caused by ice shelf bottom ablation is obtained, and finally the bottom ablation rate is estimated. According to the method, the ice shelf bottom ablation rate can be quickly and efficiently obtained from the ICESat-2 data.

The invention discloses a mountain geological disaster monitoring method and system based on remote sensing data, and relates to the field of geological disaster monitoring, and the system comprises an analysis module which is used for receiving satellite and unmanned aerial vehicle remote sensing data, and carrying out the topographic correction and noise filtering of the remote sensing data based on the mountain topographic features, extracting multi-dimensional characteristic parameters of earth surface deformation, vegetation coverage and water body distribution; the coupling module is used for receiving the multi-dimensional characteristic parameters output by the analysis module, dynamically associating the multi-dimensional characteristic parameters with the geological structure and rock-soil body property data of the mountainous area, and constructing a space-time matching and coupling calculation model of the multi-source data; according to the method, remote sensing data processing is optimized based on mountain topographic features, the multi-dimensional feature extraction accuracy is improved, data relevance is enhanced through multi-source data space-time matching and coupling calculation, disaster hidden danger points are accurately recognized and positioned in combination with dynamic weight distribution, and meanwhile the disaster development rate and the influence range are scientifically deduced according to a time sequence evolution model.

The invention discloses a physical constraint deep learning forest biomass estimation method and system. The method specifically comprises the steps of satellite-borne laser radar GEDI footprint preprocessing and quality screening; sAR radiometric calibration, terrain correction and speckle noise filtering are carried out; optical data radiation correction, atmospheric correction and geometric correction; multi-source remote sensing data space-time alignment and feature variable extraction; constructing a deep learning model fused with physical constraints; feature optimization and model reconstruction are carried out based on space-time interpretability analysis (SHAP); performing comparison and precision verification on the reconstructed final model; and generating a high-resolution annual forest biomass map based on the optimal deep learning model. The estimation result obtained by the method provided by the invention has relatively high precision and accuracy, and scientific data support and decision suggestions are provided for regional forest resource management and carbon sink capability evaluation.

The invention relates to the technical field of remote sensing image processing, computer vision and three-dimensional modeling, discloses a monocular remote sensing image building three-dimensional automatic reconstruction method based on an improved UNet network and a multi-parameter coupling model, and aims to solve the problems that a traditional reconstruction method depends on DSM or laser radar data, and is poor in complex terrain adaptability and low in automation degree. According to the method, a single high-resolution remote sensing image serves as input, building and roof pixel-level segmentation is achieved through an improved UNet network fusing a channel-space-semantic three-mode CBAM attention mechanism and a resolution adaptive SMU activation function, and footprint regularization extraction is completed in combination with contour curvature constraint and Hough transform. And constructing an elevation inversion model based on the roof and footprint offset, the imaging geometric parameters and the terrain correction coefficient, and finally generating a three-dimensional white model through a CGA parametric modeling rule. Experimental verification shows that the average absolute error of the method is about 1.06 m (the mountain scene error is smaller than or equal to 1.2 m), the height error of more than 93% of buildings is smaller than 2 m, the method does not need to depend on additional auxiliary data, the process automation degree is high, the method is adaptive to complex terrain scenes, and the method can be directly connected with a smart city and a digital twin system.

The invention provides a gravity terrain correction method and system based on an equivalent source, and relates to the technical field of geophysical exploration and measurement engineering, and the method comprises the steps: carrying out the normal field correction of actual measurement gravity data of a certain measurement point on the terrain, so as to obtain the gravity data after the normal field correction; performing intermediate layer correction on the gravity data after normal field correction to obtain gravity data after intermediate layer correction; performing height correction on the gravity data subjected to the middle layer correction to obtain gravity data subjected to height correction; establishing a uniform density point equivalent source according to the gravity data corrected by the normal field; and performing forward modeling calculation according to the established uniform density point equivalent source so as to obtain a gravity influence value of a projection position of the measuring point on the terrain on the observation reference plane. The terrain correction value is calculated according to the gravity influence value and the gravity data after middle layer correction and height correction, and the precision and reliability of the gravity data are improved.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The invention relates to the technical field of positioning, and discloses a special operation safety guarantee method and system based on Beidou high-precision positioning, and the method comprises the steps: obtaining original coordinates, signal intensity, acceleration and angular velocity, and obtaining an estimated position through data integration and complementary calculation; signal state judgment is carried out in combination with the signal intensity, position correction is carried out according to a judgment result, and an accurate position is obtained; noise suppression and motion trail prediction are carried out in combination with the acceleration and the angular velocity, and smooth trail data are obtained; carrying out deviation analysis on the track data and pre-stored historical track data, and carrying out position optimization according to a deviation analysis result to obtain optimized track data; performing terrain correction and environment compensation in combination with a pre-established geographic information database to obtain continuous trajectory data; and according to the continuous trajectory data, performing integrity optimization through time sequence alignment and data filling to obtain complete trajectory data, and outputting low-risk positioning. According to the method, the navigation reliability and safety in a complex environment are improved.

The invention discloses a reservoir plane distribution feature fine description method based on landform control, and the method comprises the steps: carrying out fine well-seismic calibration based on three-dimensional seismic data and well-seismic combination, and tracking and explaining a top interface and a bottom interface of a target reservoir; calculating an initial reservoir sensitive seismic attribute; obtaining a target reservoir deposition ancient landform; according to the fine well-seismic calibration time-depth relation, the current terrain of the target reservoir is obtained; and correcting the initial reservoir sensitive seismic attribute according to the target reservoir sedimentary ancient landform and the current terrain of the target reservoir to obtain a final target reservoir sensitive seismic attribute, and compiling a target reservoir sedimentary facies map according to the final target reservoir sensitive seismic attribute. Aiming at the reservoir characteristic analysis uncertainty caused by strong reflection of the substrate and the gas-bearing reservoir, the method for correcting the sensitive seismic attributes of the reservoir by adopting the ancient landform and the current topography is adopted, the reservoir response error information caused by strong reflection of the substrate and the gas-bearing reservoir is eliminated, the reservoir characterization precision is improved, and the reservoir analysis uncertainty is effectively reduced.

The invention relates to the field of remote sensing image processing and terrain correction, and particularly discloses a radar geometric field-driven SAR distortion information adaptive neighborhood propagation identification method, which comprises the following steps of: S1, acquiring a Golden digital elevation model of a research area and a pixel-by-pixel ellipsoid incident angle graph generated by SAR satellite precision orbit parameters and imaging geometry, and carrying out geometric unified processing on the Golden digital elevation model and the pixel-by-pixel ellipsoid incident angle graph; s2, calculating a terrain parameter, an effective slope angle, an R geometric response field and an R field directivity gradient based on the unified raster data; s3, main distortion region classification is carried out according to the effective slope angle and the pixel-by-pixel ellipsoid incident angle; s4, defining an adjacent gradient and constructing a self-adaptive threshold value and a shadow self-adaptive threshold value; and S5, performing passive region propagation from the boundary of the main distortion region, and generating a final classification grid map after space overlapping correction. According to the method, the defect of blind propagation of the existing method is overcome, and high-precision automatic identification of SAR geometric distortion under complex terrains is realized.

The invention provides an airport severe convective cloud evolution monitoring method and system based on stationary satellite multispectral characteristics, and relates to the technical field of civil aviation meteorological monitoring and early warning, and the method comprises the steps: obtaining multispectral remote sensing data of an airport monitored by a stationary satellite in real time and in a preset range; performing radiometric calibration and geometric calibration on the multispectral remote sensing data to obtain a calibrated multispectral image; extracting visible light reflectivity and infrared brightness temperature based on the calibrated multispectral image, and generating gridding feature data through spatial resampling; and based on the gridding feature data, performing semantic segmentation by adopting a full convolutional neural network integrating spatial pyramid pooling and dense up-sampling convolution so as to identify and locate a target cloud cluster with strong convection features. According to the invention, the severe convective cloud cluster is identified based on the stationary satellite data, after topographic correction and evolution prediction, graded early warning is generated in real time by fusing the airport operation state, a full-chain monitoring early warning technology is formed, and the meteorological safety guarantee capability of the airport is improved.

The invention relates to an SAR (Synthetic Aperture Radar) historical data recalibration method and an SAR historical data recalibration system based on cross of different satellites. The method comprises the following steps: acquiring calibrated satellite-borne SAR historical image data and to-be-calibrated satellite-borne SAR historical image data of different satellites with the same wave band, and performing preprocessing, terrain correction, longitude and latitude cutting and registration to generate a re-calibration historical data pair; through resolution resampling processing and incident angle correction, a recalibration cross data pair is constructed and input into a Gaussian mixture model, and a calibration coefficient of an SAR historical image is obtained through calculation. By collecting satellite-borne SAR historical image data of different satellites in the same wave band, the imaging resolution difference between different loads is solved; after processing such as preprocessing, topographic correction and cutting registration is performed on satellite-borne SAR recalibration data, clustering fitting is performed by adopting a Gaussian mixture model, and weighting fitting is performed on different data pairs to solve a calibration coefficient of to-be-calibrated historical data, so that the recalibration precision of the satellite-borne SAR historical data can be improved.

This invention discloses a machine learning-based method for reconstructing snow cover area in mountainous areas, comprising: acquiring and preprocessing optical remote sensing data and topographic data; the optical remote sensing data includes MOD09GA data and Landsat8OLI data; the preprocessing includes topographic correction and snow cover identification; using the snow cover area generated from the Landsat8OLI data using a snow cover identification algorithm as the ground truth label, and constructing a sample dataset based on the preprocessed MOD09GA data and topographic data; dividing the sample dataset into a training set and a validation set at a 4:1 ratio to train and validate an XGBoost model; the XGBoost model improves prediction performance by combining multiple decision trees; and using the trained XGBoost model to predict snow cover area on new input data. This method can generate daily 30-meter (m) snow cover area data, which has important research significance and value for mountainous snow cover monitoring, hydrological process simulation, and ecosystem evolution.

The invention relates to the technical field of natural gas gathering and transportation engineering, in particular to a rugged topography gas-liquid two-phase pipeline virtual metering method based on flow pattern self-adaption. A rugged topography gas-liquid two-phase pipeline virtual metering method based on flow pattern self-adaption is characterized in that the method comprises the following steps that an equivalent Reynolds number related to a topography correction factor is constructed, flow patterns are distinguished through the equivalent Reynolds number, and according to a flow pattern adaption and flow pattern self-adaption machine learning prediction model divided by the equivalent Reynolds number, the flow pattern correction factor is obtained; and the prediction of the gas flow is further realized. According to the method, the dependence on a physical sensor is reduced, the equipment cost is reduced, the prediction precision and adaptability of the gas flow in the fluctuating pipeline are improved, and the method can be widely applied to gas-liquid two-phase flow measurement in gas field development.

U I T T R E K S E L Disclosed is a remote sensing image enhancement method and system based on homomorphic filtering topographic correction. The method includes: after acquiring a remote sensing image to be enhanced, converting the image from a red—green—blue (RGB) color space to a hue—saturation—value (HSV) color space, and extracting a brightness component; performing homomorphic filtering processing on the brightness component; combining’ the processed. brightness component with saturation and. hue, and. inverting the component back to the RGB color space to complete image enhancement; acquiring grayscale distribution of the enhanced image, and calculating image information entropy; further calculating contrast of the image through grayscale values of pixels; calculating an enhancement score of the image based on the information entropy' and. contrast, and. comparing' the enhancement score with a preset score to determine whether the image enhancement is completed. The present invention can effectively improve the image quality of remote sensing images. (+ Fig. 1)

The invention discloses a mountain photovoltaic power station output characteristic evaluation method and system based on a guarantee rate, and relates to the technical field of photovoltaic power generation. Comprising the following steps: acquiring basic data of a mountain photovoltaic power station, the basic data comprising topographic data, meteorological data, photovoltaic module parameters and power station layout data, and preprocessing the basic data to obtain standardized data; and constructing a mountain photovoltaic power station output calculation model based on the standardized data. According to the method, the illumination intensity calculation error is reduced to be within 5% by introducing a terrain correction illumination intensity calculation model and considering the influence of gradient and slope direction on solar radiation, the precision is improved by 10-15% compared with that of a traditional plane model, meanwhile, quantization of special mountain loss factors such as temperature and dust is achieved through subentry calculation of a dynamic power loss coefficient, and the calculation accuracy is improved. And the deviation between the theoretical output and the actual output is further reduced.

The present application relates to the technical field of remote sensing information extraction and snow monitoring, and particularly relates to a sub-pixel snow mapping method based on medium-resolution remote sensing data, comprising the following steps: obtaining medium-resolution remote sensing images, a digital elevation model, and ground meteorological station snow observation data, and preprocessing the remote sensing images to obtain surface reflectivity; identifying cloud-covered pixels, filling in the cloud-free dataset by temporal and spatial interpolation and ground object spectral similarity matching; selecting snow, vegetation, and water as spectral endmembers and extracting features; constructing a linear mixing model, and using the least square method to retrieve the snow proportion in the pixel to obtain preliminary data; and generating a high temporal and spatial accuracy snow distribution map through meteorological station data verification, digital elevation model terrain correction, and model parameter optimization, which solves the problems of mixed pixels, cloud coverage, terrain errors, and the lack of accuracy verification.

This invention discloses a fully automated atmospheric and topographic radiometric correction method and device for high-resolution remote sensing imagery, belonging to the field of remote sensing science and technology. The method includes: constructing a fully automated batch processing framework; developing an atmospheric correction module based on the 6S radiative transfer model to automatically retrieve atmospheric parameters and eliminate their influence; and developing a topographic radiometric correction module based on the digital elevation model (DEM) and VECA model to compensate for radiometric distortion caused by complex terrain. The key innovation lies in constructing a joint correction framework and integrating a geomorphic feature recognition algorithm, which can automatically identify and select to execute either single atmospheric correction or a combined atmospheric-topographic correction process based on the degree of topographic relief in the image. This invention achieves fully automated batch processing from data input to result output, requiring no manual intervention, significantly improving the processing efficiency, intelligence level, and adaptability to different terrain scenarios in high-resolution image radiometric correction.