135 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 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 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 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 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 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-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 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 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 data fusion, and particularly provides a multi-scale eco-hydrological parameter joint inversion method and system based on unmanned aerial vehicle and satellite-ground data fusion, and the method comprises the steps: obtaining multispectral, hyperspectral and thermal infrared data of a research region through a high-resolution sensor carried by an unmanned aerial vehicle; the method comprises the following steps: acquiring unmanned aerial vehicle data, acquiring satellite remote sensing data, performing radiation correction on the unmanned aerial vehicle data and the satellite data, and performing feature extraction and space-time fusion on the unmanned aerial vehicle data and the satellite data by using a deep learning algorithm to generate high-resolution fusion data; through a multi-resolution analysis or spatial interpolation technology, coordinating differences of unmanned aerial vehicle data and satellite data in space and time scales, and constructing a multi-scale data set; based on a physical model and a data driving model, combined with fusion data, an inversion result is visually displayed, and the method has the effects of meeting the hydrological monitoring requirement of high time resolution, and particularly providing real-time monitoring data in the hydrological process of rapid change.

The invention provides a dry-farming oat growth vigor monitoring method and system based on unmanned aerial vehicle hyperspectral remote sensing, and relates to the technical field of dry-farming oat growth vigor monitoring. The reflectivity of each image pixel under different spectral band conditions is calculated through the absolute radiation brightness and the known reflectivity of the reference plate, the average reflectivity of the unit grids in different spectral bands is obtained, a linear product is adopted to construct a growth score index, the growth score index of each grid unit is clustered by adopting a K-means clustering method, and the average reflectivity of each grid unit is calculated. And outputting the clustering condition of the growth vigor of the dry farming oats of the grid units, and setting a judgment threshold according to the clustering condition of the growth vigor of the dry farming oats to realize growth vigor classification. A reference plate is introduced to calibrate the reflectivity, the pixel-level absolute reflectivity is calculated through a radiation transfer equation, and the influence of illumination condition changes is eliminated; k-means clustering is adopted to automatically divide growth vigor levels, and a threshold value can be generated in a self-adaptive mode based on data distribution.

The invention relates to a fertilization prescription map rapid generation method and system based on multispectral detection, and belongs to the technical field of agriculture. Comprising the following steps: multi-source data acquisition: synchronously acquiring a 450-900nm wave band crop multi-spectral image acquired by a multi-spectral camera carried by an unmanned aerial vehicle, laboratory detection nutrient data of a preset soil sampling point, real-time soil humidity data, plot boundary vector data and a crop recommended fertilization model; data preprocessing: carrying out radiation correction, geometric correction and de-noising processing on the multispectral image; performing crop nutrient demand inversion; according to the method, the multi-spectral image of the unmanned aerial vehicle, the soil sampling data, the real-time soil humidity and the plot boundary vector data are synchronously integrated, a raster data set of a unified coordinate system is constructed, and crop nutrient requirements and environment variables are comprehensively covered. In combination with vegetation index combinations such as NDVI, RENDVI and RVI, the crop nutrient content is inverted through the PLSR algorithm, and the prediction precision R2 is greater than 0.85 and is significantly superior to that of a single index model.

The invention discloses an electric power gallery flood risk assessment method and system based on deep learning, and belongs to the field of flood disasters, and the method comprises the steps: achieving the water extraction and risk grade assessment of a radar image, obtaining the radar image from a Sentinel-1 satellite, removing the noise through a Speckle filter, and carrying out the geometric correction, radiation correction and normalization processing. Data preprocessing is carried out to ensure data quality; performing image segmentation on the preprocessed image by using a U-Net model, extracting and recovering image features through an encoder and decoder structure, and realizing high-precision water body segmentation through a deep learning model; the water body extraction precision is evaluated by calculating indexes such as a confusion matrix, a recall rate, accuracy and MIOU, the flood risk level of the power equipment is evaluated according to the extracted water body data, the gallery bridge distance and the gradient, and risk evaluation is divided into five levels. According to the method, the problem of how to accurately extract the water body and evaluate the flood risk of the electric power gallery is solved, and a scientific basis is provided for protection and management of electric power facilities.

The invention relates to the field of agriculture, and discloses a method for evaluating sugarcane smut based on an unmanned aerial vehicle image, which comprises the following steps: data acquisition: planning a route by using an unmanned aerial vehicle carrying a multispectral camera, a hyperspectral camera and an RGB (Red, Green and Blue) camera, so that the unmanned aerial vehicle acquires image data of sugarcane; preprocessing: sequentially carrying out geometric correction, radiation correction and image fusion processing on the image data to obtain preprocessed data; feature fusion: constructing a fusion model including spectral feature extraction, visual feature extraction and spatial-temporal feature fusion according to the feature expression of the sugarcane smut; and model generalization: collecting multi-source images and disease investigation data according to the actual condition of the sugarcane. The method comprises the following steps: performing data acquisition through an unmanned aerial vehicle, obtaining multi-dimensional image data in a sugarcane growth critical period, performing preprocessing, constructing a fusion model including spectral feature extraction, visual feature extraction and spatial-temporal feature fusion, performing fine adjustment and optimization in combination with a transfer learning strategy, and performing real-time analysis on a disease detection result.

The invention discloses a remote sensing monitoring method for a peanut planting state, and belongs to the technical field of data processing. The method specifically comprises the following steps: S1, historical data acquisition and screening: acquiring actual measurement historical data of satellites, unmanned aerial vehicles and the ground in a target area, and performing quality screening; s2, historical data preprocessing: performing radiation correction, geometric precise registration, space-time alignment and numerical value standardization processing on the acquired historical data; through a three-stage data acquisition network of'satellite + unmanned aerial vehicle + ground sensor ', the complementation of'macroscopic area coverage + microscopic plot details' is realized, the large-scale monitoring requirement is met, and the subtle changes of key growth periods such as the peanut seedling period and the peanut flowering period can be captured; and meanwhile, Sentinel-1 microwave data and optical data are fused, so that the problem of weather dependence of the optical data is solved.

The invention discloses a glacier mass balance calculation method for generating an elevation time sequence based on ASTER stereo data. The glacier mass balance calculation method comprises the following steps of: 1, collecting a glacier ASTER L1A stereo image and preprocessing the glacier ASTER L1A stereo image; 2, the quality of the ASTER stereoscopic image pair is improved through radiation correction and strip removal processing; 3, calculating a rational polynomial coefficient RPC model of the ASTER stereo image pair, and detecting and correcting a jitter error of a satellite in a vertical orbit direction; 4, performing residual deviation correction by using the reference ASTER DEM data; 5, high-precision ASTER DEM data are obtained through abnormal value filtering; 6, generating time-continuous ASTER DEM data by adopting a Gaussian process regression method; and 7, calculating the volume change of each glacier, and polymerizing the volume change of each glacier to obtain the overall mass balance change of the regional glacier. According to the method, high-precision and time-continuous ASTER DEM data are generated through correcting satellite jitter errors by a system, and abnormal value filtering and statistical aggregation are combined, so that the mass balance change of the large-scale regional glacier is accurately calculated.

The invention discloses a remote sensing satellite in-orbit relative radiation calibration method and system considering geometric radiation characteristic constraints. The method comprises the following steps: S1, finishing time sequence image alignment based on geometric information constraints; s2, estimating a time sequence image earth surface truth value based on geometric radiation characteristic constraints among the images; s3, extracting a gray value of each detection element of the area array on each frame sequence; s4, constructing an area array load radiation calibration model; and S5, radiometric calibration model resolving is carried out, in-orbit relative radiometric calibration work of the area array type remote sensing satellite is completed through the steps S1-S4, an in-orbit relative radiometric calibration coefficient is obtained, and input is provided for radiometric correction of an original image. The method is suitable for in-orbit relative radiometric calibration of area array type remote sensing satellites, and has the advantages of high frequency, high efficiency, more convenience and low cost.

This invention provides a method and system for constructing a 3D model of a construction site by combining multiple data sources. It relates to the field of data processing technology. The method includes: retrieving satellite imagery at a preset resolution to obtain a full-area stereo image pair dataset; performing radiometric correction to generate a regional surface model and a regional elevation model; performing raster-level modeling defect detection to locate distributed blind zone blocks; fitting obstacle avoidance paths to generate optimized obstacle avoidance flight trajectories; driving a UAV to perform multi-angle surround scanning to directionally collect dense point clouds in the distributed blind zones; constructing a distributed blind zone model; and performing microscopic overlay to output a 3D model of the construction site. This invention solves the technical problems of existing construction site modeling methods, which have limitations in coverage, modeling efficiency, and data real-time performance, and rely on a single data source, failing to provide a comprehensive and accurate 3D model of the construction site, thus affecting construction progress and decision-making.

The present application relates to the technical field of data processing, and discloses a method and system for extracting seawall feature information based on multi-source remote sensing data. The method includes: obtaining standardized data by performing band combination enhancement, geometric correction, and radiometric correction on multi-source remote sensing images; extracting seawall targets using multi-scale segmentation; identifying structural features through profile analysis and spectral matching; calculating vegetation indices and texture features to obtain ecological information; establishing an accuracy evaluation system; and finally constructing a feature information database. The present application improves the accuracy and efficiency of seawall feature information extraction through technical means such as multi-source data fusion processing, multi-scale feature extraction, and ecological feature evaluation, and solves the problems existing in data acquisition, processing, and evaluation of traditional methods.

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

This disclosure discloses a method and system of hue-distance constrained relative radiometric correction considering Helmholtz-Kohlrausch (H-K) effect for multiple remote sensing images. The method comprises: first converting the image from the RGB color space to the CIELAB color space, then performing relative radiometric correction using adaptive constraint based on hue-distance, and quantitatively describing the H-K effect to apply global lightness mapping to the corrected image. Based on the limitations of the RGB color space, this disclosure proposes a relative radiometric correction strategy that maintains hue distance, which suppresses radiometric anomalies in local regions and optimizes correction results. By implementing global lightness mapping based on the H-K effect and introducing perceived lightness into the relative radiometric correction of remote sensing images, this method effectively enhances the radiation consistency of multi-temporal images.

This invention relates to the field of detection, and in particular to an online method and system for detecting impurities in calcium carbonate ore. Specifically, it includes: acquiring a continuous multispectral image sequence of calcium carbonate ore on a conveyor belt, and performing radiometric correction and spectral normalization preprocessing; identifying dominant spectral clusters of pure calcium carbonate ore by statistically analyzing the pixel spectra of the preprocessed images, and selecting a subset of high-confidence pixel spectra as the initial background training set; training a background dictionary, applying a set of sparse regularization constraints during training, and using the correlation between spectral bands to constrain the atomic energies of the dictionary to continuous or strongly correlated band groups; for each pixel of the detected image, using a sparse approximation algorithm combined with the background dictionary, calculating a sparse reconstruction coefficient vector and a reconstruction residual vector, and obtaining the reconstruction residual and coefficient activation uncertainty measure, respectively; if the reconstruction residual exceeds a first threshold, or the uncertainty measure exceeds a second threshold, it is determined to be an impurity point, and the location and quantity are summarized to generate an online impurity distribution map.

The invention discloses an optical remote sensing satellite image local stripe noise rapid relative radiometric calibration method, belongs to the technical field of optical remote sensing images, and solves the technical problem that the newly added vertical stripe noise of the image local part cannot be rapidly and effectively corrected due to sudden change of the imaging state of a sensor during the in-orbit operation of an optical remote sensing satellite. Comprising the following steps: searching a calibration image with a uniform ground object covering strip position; performing relative radiation correction on the image by using the original calibration coefficient; calculating a stripe noise calibration coefficient based on the uniform region image; correction is carried out according to the laboratory relative radiometric calibration coefficient and the on-orbit relative radiometric calibration coefficient, the updated calibration coefficient can adapt to the changed imaging state of the sensor, and newly added local stripe noise of the image is effectively eliminated.