167 results about "Radiometric correction" patented technology

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention discloses a remote sensing image ship detection method based on SPD-YOLOv8, which belongs to the technical field of satellite remote sensing, is used for ship detection, and comprises the following steps: acquiring a satellite remote sensing image ship data set in an experimental sea area, cutting a remote sensing image, and performing radiation correction and geometric correction; dividing the data set into a training set, a test set and a verification set; performing image enhancement on the data set; an SPD-YOLOv8 ship target detection model is constructed; performing ship target detection by using the ship target detection model; parameter tuning and result evaluation are carried out; deploying the ship target detection model to a server; and abnormal behaviors and illegal activities are detected and early warned. Compared with the prior art, the method has the advantages that the detection effect of low-resolution images and small targets is improved, the accuracy, real-time performance and response speed of the detection model are improved, ship targets of different sizes and types can be learned and generalized more effectively, and the robustness and practicability of a detection algorithm are improved.

The invention relates to the technical field of data processing, and discloses a seawall feature information extraction method and system based on multi-source remote sensing data. The method comprises the following steps: carrying out wave band combination enhancement, geometric correction and radiation correction on a multi-source remote sensing image to obtain standardized data; extracting a seawall target by using multi-scale segmentation; recognizing structural features through profile analysis and spectrum matching; calculating vegetation indexes and texture features to obtain ecological information; establishing a precision evaluation system; and finally constructing a feature information database. Through the technical means of multi-source data fusion processing, multi-scale feature extraction, ecological feature evaluation and the like, the precision and efficiency of seawall feature information extraction are improved, and the problems existing in the aspects of data acquisition, processing and evaluation in a traditional method are solved.

The invention relates to the technical field of gas leakage detection, and discloses an infrared imaging radiation correction method and system for gas leakage detection, and the method comprises the steps: obtaining the distribution data of dark current noise, and the mapping relation parameters of an image gray value and a radiation brightness value; acquiring infrared radiation of the gas leakage scene by using an infrared detector to obtain an original infrared image; based on the distribution data of the dark current noise, performing pixel-by-pixel compensation on the original infrared image to generate an infrared image after dark current correction; extracting the radiation intensity distribution of the infrared image after dark current correction, calibrating the radiation value of each pixel in the infrared image based on the mapping relation parameter between the image gray value and the absolute radiation brightness value, converting the image gray value into the absolute radiation brightness value, and generating the infrared image after radiation calibration; and outputting an infrared image after radiometric calibration for subsequent gas leakage area identification and feature analysis. According to the invention, a high-quality and quantifiable radiation quantification image can be output.

The present invention relates to the technical field of algae content monitoring. Disclosed is an algae content information monitoring method based on multi-source remote sensing data. The present invention comprises: acquiring multi-source remote sensing data comprising satellite remote sensing data, unmanned aerial vehicle remote sensing data and ground monitoring data, and preprocessing the data, involving radiation correction, geometric correction and noise elimination; and fusing the multi-source data into a trained algae monitoring model for prediction, and using Kalman filtering to assimilate observation data and model prediction data. The present invention fuses observation data and model prediction data by means of Kalman filtering technology, so as to dynamically adjust the model state, such that the model can more accurately reflect the actual observation situation. The Kalman filtering optimizes the real-time prediction capability of models by balancing the uncertainties between observation data and prediction data, thereby ensuring the accuracy and real-time performance of monitoring results, and also ensuring the reliability and adaptability of models under various environmental conditions.

The invention discloses an implementation method of an unmanned aerial vehicle remote sensing image enhancement system based on multi-source data fusion. The method comprises the following steps: S1, carrying optical and thermal imaging sensors through an unmanned aerial vehicle, performing autonomous flight and collecting data; s2, denoising, geometric correction and radiation correction are carried out on the preprocessed data; s3, performing contrast stretching, histogram equalization and edge detection by using an image enhancement algorithm; s4, extracting topographic features from the enhanced image, and generating a three-dimensional digital elevation model and a digital topographic map; and S5, performing multi-source data fusion, integrating high-precision topographic data into a geographic information system, and supporting map making and spatial analysis. Through the multi-sensor fusion and image processing technology, the image resolution and surveying and mapping precision of the complex terrain area are improved, detailed and accurate terrain information is provided, various applications such as environment monitoring, urban planning and disaster assessment are supported, and the practical value is achieved.

The invention discloses a multi-source remote sensing and unmanned aerial vehicle combined wetland monitoring method, equipment and a medium, relates to the technical field of wetland monitoring, and realizes efficient and accurate wetland monitoring through combination of satellite remote sensing and unmanned aerial vehicle technologies. Firstly, space-time continuity and noise suppression preprocessing is carried out on a satellite remote sensing image, and the image quality is improved; thirdly, the image is optimized through segmentation fusion processing, and the species distribution range and the community boundary are accurately extracted; the unmanned aerial vehicle monitoring path is planned based on the data, and the monitoring efficiency is improved. A high-resolution multispectral image obtained by the unmanned aerial vehicle is subjected to radiation correction and geometric correction and then is fused with a satellite remote sensing image to generate a high-precision wetland species distribution diagram. According to the scheme, the advantages of large-range coverage of satellite remote sensing and high resolution of the unmanned aerial vehicle are fully played, the defects of data quality, information extraction and monitoring efficiency in a traditional method are overcome, and powerful support is provided for wetland protection and management.

The invention provides a large-scale land cover remote sensing intelligent information extraction method and system based on sequence mask prediction, and aims to improve the precision and efficiency of land cover information extraction. The method comprises the steps of preprocessing and region division, sample set generation and labeling, U-ConvHDNet model training, sequence mask prediction, model precision evaluation and the like. Through preprocessing of geometric correction, radiation correction and the like, fine region division is constructed in combination with multiple data sources. A sample set is independently manufactured and labeled, a U-ConvHDNet model is used for binary classification training, ConvNeXt, Haar wavelet down-sampling and up-sampling modules DySample are fused in the model, and deep features and multi-scale information are effectively captured. In the sequence mask prediction stage, a double receptive field seamless sliding window is adopted, and step-by-step prediction is carried out in combination with the ground feature extraction difficulty. The method and the system have the characteristics of high automation degree, high extraction precision, strong adaptability and the like, and can be widely applied to the field of large-scale land utilization mapping and monitoring.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) image processing, and particularly discloses an SAR image terrain radiation correction method considering overlay and shadow regions, which comprises the following steps: acquiring an SAR image, a DEM (Digital Elevation Model) image and a source file in a research region; carrying out multi-view processing on the original SAR image, resampling the DEM image, and carrying out radiometric calibration; geometric positioning is carried out according to the RD model by adopting an indirect positioning method, lookup tables A and B are generated, and images are registered; detecting pixels of the overlay area and the shadow area; calculating an effective scattering area corresponding to each SAR pixel; and performing radiation correction according to the effective scattering area, repairing a back scattering coefficient value of the shadow area, and finally performing geometric correction. According to the method, the topographic effect caused by an SAR imaging mechanism and topographic relief is eliminated to a certain extent, the radiation correction effect of an area with large topographic relief is improved, and the corrected product can be widely applied to the fields of ground feature inversion and classification, deformation detection, target detection and the like.

The invention provides a correction method and system for anisotropic reflection of GF6-WFV data, and belongs to the technical field of spatial information.The correction method comprises the steps that the GF6-WFV data is subjected to block processing, data of a block center is compared with data extracted from satellite navigation file data, atmospheric correction is carried out on the block data one by one, and the correction efficiency is improved; the processing efficiency and the correction precision are remarkably improved, image data are divided into small blocks with 2000 * 2000 pixels through blocking processing, the pixel amount calculated each time is reduced, the method is more suitable for rapid processing of large-data-amount and high-resolution images, the blocking coverage range is large, local and overall consistency can be taken into account, and secondly, the processing efficiency and the correction precision are greatly improved. And the zenith angle and observation angle parameters of each block are standardized by the angle normalization model, so that the angle difference between different areas is reduced, and the accuracy of a correction result is improved. And finally, in combination with a lookup table technology, computing resource consumption can be remarkably reduced, and the method is convenient to operate in practical application of radiation correction and has the advantages of high efficiency and convenience.

The invention discloses a method for analyzing a climate change rule based on remote sensing data. Multi-source remote sensing data are obtained through multiple platforms of satellites, aviation and ground, and after preprocessing of geometric correction, radiation correction and the like, features of surface temperature, humidity and the like are extracted through a split window algorithm, a polarization difference index method and the like. Principal component analysis is used for dimensionality reduction, partial least squares regression is used for determining the quantitative relation between features and climate changes, and a correlation model is established by considering human activity influences. The method comprehensively obtains data, improves the processing and analysis precision, establishes the relation between human activities and climate changes, can accurately determine the climate change rule, and provides powerful support for climate change research and response.

The invention belongs to the technical field of ocean remote sensing and deep learning, and particularly relates to an SAR image ocean internal wave automatic extraction method which comprises the following steps: step 1, collecting SAR image data of a target sea area, and preprocessing the image, including radiation correction and geometric correction; 2, marking internal wave signals in the preprocessed SAR image in a manual marking mode, generating a truth value image used for deep learning model training, matching a truth value with an original image, randomly selecting four fifths of the truth value image as a training set, and randomly selecting the rest one fifths of the truth value image as a test set; by adopting the alpha-balanced cross-entropy loss function and the online data enhancement technology, the precision and efficiency of extracting the internal wave signal from the SAR image are effectively improved, especially under the complex imaging condition, the internal wave signal can be accurately extracted, high-precision geographic positioning of the internal wave signal is achieved by combining geographic information, and the method has the advantages of being high in accuracy and high in accuracy. The method is suitable for ocean research and high-precision environment monitoring, and has a wide application prospect.

The invention discloses a leakage gas cloud three-dimensional reconstruction method, and relates to the technical field of gas cloud image processing. According to the leaked gas cloud three-dimensional reconstruction method, through combination of a multispectral image data cube and a pre-trained Yolov5 model, precise identification and analysis of a gas cloud leakage area are realized, through radiation correction and adaptive filtering processing, absorbance data of the gas cloud are extracted and fitted, concentration distribution of each layer of gas cloud is further obtained, and the three-dimensional reconstruction of the leaked gas cloud is realized. By stacking data of each layer and applying a three-dimensional reconstruction algorithm, an accurate gas cloud three-dimensional structure is generated, high-precision spatial distribution information is provided for gas leakage monitoring, the monitoring and response efficiency is remarkably improved, the limitation of a traditional method in the aspects of precision and adaptability is overcome, and by integrating multi-view and multi-band image data, the real-time performance of gas leakage monitoring is improved. The gas cloud leakage area can be identified more accurately, and the gas cloud concentration distribution is accurately obtained through asymmetric Gaussian function fitting, so that the gas leakage monitoring precision is improved.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

The invention discloses a multi-source data assimilation method based on remote sensing in the technical field of remote sensing data, and the method comprises the following steps: S100: data preprocessing: carrying out the preprocessing of multi-source remote sensing data, including radiation correction, geometric correction and cloud detection, so as to eliminate the noise and error in the data; and S200, feature extraction: extracting useful feature information from the preprocessed data, including spectral features and texture features, so as to facilitate subsequent data analysis and application. And the prediction capability is improved: the model prediction capability can be improved through data assimilation, so that a model result is continuously close to an observation value, and the prediction accuracy is improved. The observation data of different sources and resolutions are fused through data assimilation, information more accurate and richer than single data can be obtained, fuzziness is reduced, and interpretation reliability is improved.

The invention provides a multi-modal alignment and cross-scale structure attention-based remote sensing image building extraction method, which comprises the following steps of: performing radiation correction and geometric correction on an original hyperspectral satellite image, and performing voxelization, normalization and registration with a hyperspectral corrected image on LiDAR point cloud data to respectively obtain the hyperspectral corrected image and LiDAR corrected data; deep features of the two types of correction data are extracted through a special encoder, and after dimension unification and channel attention screening, fusion features are obtained through fusion of a weighting and gating mechanism; multi-scale features are extracted from the fusion features through multi-receptive field convolution branch, and cross-scale enhancement features are obtained by combining cross attention and geometric modeling; a prediction edge graph is generated based on cross-scale enhancement features, high-resolution guide features are combined to compensate tiny building information, and the extraction precision is improved through edge loss optimization; and carrying out denoising, edge repairing and adhesion separation on the optimized result to obtain a final building extraction result.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention provides a satellite observation data restoration method and device based on generation of a collaborative adversarial network, and relates to the technical field of data restoration, and the method comprises the steps: extracting a cloud coverage sub-region and a cloud-free sub-region from a target region, and carrying out the cloud coverage sub-region and the cloud-free sub-region based on respective corresponding current satellite observation data subsets; performing space-time registration and radiation correction on the historical satellite observation data subset corresponding to the cloud coverage sub-region under the condition of no cloud coverage to obtain a corrected historical satellite observation data set corresponding to the target region; and performing progressive cooperative training on the generated cooperative adversarial network by using the current satellite observation data set of the target area and the corrected historical satellite observation data set. According to the method, the problems of poor cloud coverage data missing repair effect and insufficient physical consistency in a traditional method can be relieved.

The invention provides a slag field earth-rock volume measuring and calculating device and method based on close-range photogrammetry, and relates to the technical field of close-range photogrammetry. Comprising a photogrammetric device, the photogrammetric device is used for shooting image data of a slag field, the photogrammetric device comprises a digital camera, a tripod, a holder and a GPS and GNSS positioning system, and an image processing system is used for carrying out preprocessing, distortion correction, radiation correction and denoising processing on the shot image data. According to the technical scheme, by using the high-resolution camera and an advanced image processing algorithm, the device can more accurately capture details on the surface of the slag field, so that errors are reduced when a three-dimensional model is constructed, the high-precision three-dimensional model can more accurately reflect the actual shape and the volume of an earth-rock cube, and the accuracy of the three-dimensional model is improved. The calculation result of the earth-rock volume is more accurate, which is crucial for engineering management and resource planning because the method can reduce cost waste or resource shortage caused by inaccurate estimation.

The invention provides a satellite-borne synthetic aperture radar in-orbit radiation correction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining echo data of a synthetic aperture radar, and obtaining a first antenna pattern; preprocessing the echo data to obtain preprocessed echo data; performing compression processing on the first antenna pattern to obtain a two-dimensional lookup table; constructing a comprehensive compensation factor according to the two-dimensional lookup table; introducing the comprehensive compensation factor into a pre-constructed first imaging model to obtain a second imaging model; and inputting the preprocessed echo data into the second imaging model to obtain target echo data. According to the invention, the echo data of the synthetic aperture thunder is corrected before imaging, the processing flow is simplified, the consumption of computing resources is reduced, the time consumption is shortened while the precision is improved, and the imaging efficiency is improved.

The invention belongs to the technical field of image processing, and particularly relates to a multi-source data-driven flood storage and detention area ecological toughness detection method and system, and the method comprises the steps: 1, carrying out the geometric correction and radiation correction of an optical satellite remote sensing image, and forming a time sequence basic data set; 2, a first axis and a second axis of the ecological state matrix correspond to space grids of the flood storage and detention area respectively, and a third axis corresponds to time; 3, mapping the ecological state matrix to a fractal space to form a time coupling matrix; 4, for each time slice of the time coupling matrix, setting a neighborhood with a fixed size around each pixel, and performing time window segmentation and fractal complexity estimation on the multi-scale morphological gradient map; and step 5, calculating the toughness value of the connected region according to the area and the toughness values of all positions in the connected region. According to the method, the accuracy and practicability of ecological toughness detection are remarkably enhanced.

The invention relates to the technical field of synthetic aperture radar signal processing, and discloses a satellite-borne SAR image in-orbit radiation and geometric correction method based on a parallel computing architecture, and the radiation correction method comprises the steps: obtaining ephemeris parameters of a satellite under a geocentric coordinate system at all azimuth moments; calculating a slant distance between a satellite beam center pointing scene center point and an antenna phase center at each azimuth moment and a slant distance between each sampling point and a satellite in an SAR echo distance direction at each azimuth moment; calculating satellite beam center pointing at each azimuth moment and a lower visual angle between the satellite and a sub-satellite point connecting line; calculating an antenna distance direction beam directional diagram and constructing a radiation correction function; and performing parallel correction on radiation distortion of the SAR image based on the radiation correction function. According to the method, the SAR image quality and the accurate positioning of the SAR image are effectively improved, the radiation fidelity of the SAR image is met, meanwhile, the efficiency of a data processing task is improved, and the task requirement for high real-time performance is considered.

The invention discloses a film thickness measurement method and system based on a line scanning hyperspectral camera, and relates to the technical field of hyperspectral imaging, and the method comprises the steps: obtaining a reflection or transmission spectrum image and a dark noise image, and carrying out the denoising processing; using a hyperspectral camera and an integrating sphere to carry out radiation correction on the reflected or transmitted spectral image; acquiring a film reflectivity or transmissivity spectrum according to the transmission spectrum without the film or the standard specular reflection spectrum; and determining the number of global interference periods according to a reflectivity or transmissivity curve, and calculating by using a fast Fourier transform method or a spectrum fitting method to obtain the film thickness. Image data with space and spectrum information is acquired through a hyperspectral camera, high-precision film thickness measurement under global imaging and dynamic scanning is realized through an algorithm combining fast Fourier transform and spectrum fitting, and meanwhile, the film thickness measurement precision is improved by adopting dark noise correction and integrating sphere radiation correction. The quantitative accuracy of incident light and sample reflection or transmission light is ensured, and the detection efficiency is greatly improved.

The invention provides a building change detection method and system based on a twinborn change attention residual network. The method comprises the following steps: acquiring original remote sensing images of two stages of research areas; performing geometric registration and radiation correction on the original remote sensing image in sequence to obtain two time-phase feature maps; and inputting the two time phase characteristic graphs into a twinborn change attention residual network based on ResNet and UNet to obtain a change probability graph. The scheme provided by the invention has higher robustness and accuracy in a building change detection task.

The invention discloses an image real-time radiation correction method for a push-broom hyperspectral camera, and relates to the technical field of image processing methods. The method comprises the following steps: on the basis of an established real-time radiation correction system, defining acquisition parameters, and collecting hyperspectral data of four standard reflectivity plates by using a hyperspectral camera; the hyperspectral image and spectrograph information are aligned through a timestamp, and an ROI region method is established to complete image preprocessing of the push-broom hyperspectral camera; the information collected by the spectrometer is corrected by correcting the azimuth angle of the cosine corrector, and a hyperspectral correction model is established to predict the reflectivity. According to the method, data acquisition is accurate, real-time radiation correction of the hyperspectral image can be realized, and the correction precision is high.

The invention provides a multi-data combined construction site three-dimensional model construction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the calling of a preset resolution satellite image, and obtaining a full-region stereo image pair data set; after radiation correction is carried out, a region surface model and a region elevation model are generated; grid-level modeling defect detection is carried out, and distributed blind area blocks are positioned; performing obstacle avoidance path fitting to generate an obstacle avoidance optimized flight path; the unmanned aerial vehicle is driven to execute multi-angle surrounding scanning, and distributed blind area dense point clouds are directionally collected; constructing a distributed blind area model; and carrying out microscopic superposition, and outputting a three-dimensional model of the construction site. The technical problems that a construction site modeling method in the prior art is limited in the aspects of coverage, modeling efficiency and data real-time performance, depends on a single data source, cannot provide a comprehensive and accurate three-dimensional model of a construction site, and affects the construction progress and decision are solved.

The invention provides a sea reclamation change remote sensing image target classification method and system, and the method comprises the steps: obtaining a remote sensing satellite image of a monitored sea reclamation region, carrying out the geometric correction, radiation correction and tide level normalization correction of the remote sensing satellite image, and carrying out the fusion of the corrected remote sensing satellite images; a fused remote sensing satellite image is cut into training slices for training, an MCFCN model is optimized, test slices are predicted, and CRF correction and morphological corrosion post-processing and splicing are carried out to form a final result. Through the method and the corresponding system, the segmentation efficiency and the detection accuracy of the sea reclamation change remote sensing image can be improved.

The invention relates to the technical field of remote sensing monitoring and climate analysis, in particular to a high-altitude area surface albedo remote sensing monitoring method and device, and the method comprises the steps: firstly obtaining the spectrum, infrared and topographic data of the surface, carrying out the radiation correction and topographic feature extraction, and generating preprocessing data; thirdly, generating multi-dimensional albedo data by using a multi-level fusion technology and an albedo calculation model, and obtaining joint albedo data through frequency domain and time domain analysis; and finally, generating a high-precision albedo product through a self-adaptive deep learning model and a residual error correction mechanism, and applying the high-precision albedo product to climate monitoring, ecological assessment and disaster early warning of a high-altitude area. According to the method, the accuracy and temporal-spatial resolution of albedo data are effectively improved, and reliable data support is provided for environmental research of high-altitude regions.