277 results about "Atmospheric correction" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention discloses an urban land subsidence analysis method and system based on multi-source data fusion, relates to the technical field of subsidence analysis, and constructs a multi-source three-dimensional monitoring network, performs air-space-ground multi-source data fusion analysis and atmospheric delay error correction, constructs a land subsidence prediction model, and provides an urban land subsidence analysis method and system based on multi-source data fusion. According to the method, a ground surface and underground three-dimensional monitoring network is constructed, the level, layered settlement and underground water data are integrated, minute-level global monitoring is achieved in combination with the Internet of Things, and the method is high in practicability and high in practicability. Through air-sky-ground data fusion and atmospheric correction, monitoring errors are reduced to a millimeter level, an evaluation system combining subjective and objective weights generates a five-level risk map, a visual platform supports dynamic simulation and factor traceability, a whole-process technical closed loop is formed, and the intelligent level of urban settlement prevention and control is remarkably improved.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention relates to the technical field of agriculture, and discloses a soil fertility remote sensing inversion method based on machine learning. Acquiring and preprocessing a historical soil database, historical remote sensing image data and target year remote sensing image data, including radiometric calibration, atmospheric correction and geometric correction; calculating a vegetation index and a leaf area index based on the preprocessed historical remote sensing image data, and determining a bare soil window period; creating a fishing net grid covering the research area based on the spatial distribution of the bare soil window period; historical soil parameters and remote sensing image wave band reflectivity values of corresponding window periods are extracted from the fishing net grids, and training samples are constructed; the training samples are divided into a training set and a verification set, and feature selection is carried out through a correlation coefficient method; establishing a soil parameter inversion model based on the training set by adopting a multi-model cooperative training mode; and performing soil fertility inversion on the remote sensing image data of the target year by using the trained model. In conclusion, the prediction precision of the soil fertility can be improved.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses an ecological environment detection method and system based on multispectral remote sensing fusion, and relates to remote sensing image processing. The method comprises the following steps: collecting multispectral remote sensing image data of a target area; performing multiband joint atmospheric correction processing on the multispectral remote sensing image according to the scattering coefficient, the atmospheric light value and the transmissivity of each band; performing foreground and background analysis on the corrected multispectral remote sensing image; fusing the vegetation area and the non-vegetation area of each wave band image by adopting different weight strategies to generate a multispectral fusion image; and extracting spectral features of the multispectral fusion image, constructing a standard vegetation spectral feature library, and identifying regions deviating from a standard vegetation spectrum through an anomaly detection algorithm according to the extracted spectral features to obtain various vegetation coverage rates. In view of low vegetation identification precision caused by direct foreground and background division of a multispectral remote sensing image under an atmospheric interference condition, vegetation division is performed after a clear image is obtained, so that the detection precision is improved.

The invention discloses an atmosphere and sea color parameter joint inversion method under sparse remote sensing satellite data. The method comprises the following steps: firstly, acquiring various satellite remote sensing and sea color data in a unified format; secondly, using the obtained satellite remote sensing and sea color data to construct a sparse atmosphere sea color data set through pixel mask and combining observation geometric information carried by the satellite remote sensing data; then, based on remote sensing waveband data processing, a sparse remote sensing depth inversion network comprising a sparse processing network, an external depth estimation network and a scale fusion network is constructed; and finally, training the sparse remote sensing depth inversion network through the sparse atmospheric sea color data set, outputting an inversion result image, and performing model evaluation. According to the method, joint estimation of atmosphere and sea color multiple parameters can be realized, the inversion precision in a complex ocean and atmosphere interaction scene is improved, and reliable technical support is provided for high-precision atmosphere correction and ocean carbon flux estimation.

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.

The invention relates to the technical field of atmospheric correction and remote sensing, in particular to an aerosol optical thickness obtaining method and system, and the method comprises the steps: obtaining the high-frequency historical aerosol optical thickness of a target region through the inversion of historical multi-angle multi-band polarization data; performing space-time matching on the high-frequency historical aerosol optical thickness and historical data to obtain a training data set; and obtaining the high-frequency aerosol optical thickness of the target area by using the trained neural network model. According to the invention, through the inversion data of the multi-angle multi-band polarization data, the preset neural network model is trained with the geographic data, the meteorological data and the ground base station network data, the relationship between the geographic data, the meteorological data and the ground base station network data and the aerosol optical thickness is established, and a plurality of results are fused, so that the space-time coverage is improved, and the accuracy of the aerosol optical thickness is improved. The defects of a traditional aerosol optical thickness product in time and space continuity are effectively overcome.

A high temporal-spatial resolution vegetation index fusion method based on a multi-source optical satellite image comprises the following steps: firstly, performing radiometric calibration, atmospheric correction and geometric fine correction on Landsat, Sentinel-2 and MOD09A1 data, and unifying temporal-spatial resolution to 10m / 8 days; pixel-level fusion is carried out by adopting an improved continuous correction method, a correction coefficient K is introduced to compensate Sentinel-2 critical period data defect influence, and fusion precision is improved through dynamic weight adjustment; and finally, a continuous and smooth EVI time sequence is constructed by using cubic spline interpolation and Savitzky-Golay filtering. According to the method, single-source data space-time limitation is broken through, after fusion, the vegetation index spatial resolution reaches 10 m, the time resolution reaches 8 days, the key phenological period extraction error is smaller than or equal to 3 days, the crop classification precision is larger than or equal to 90%, the accuracy and continuity of farmland-scale vegetation monitoring can be remarkably improved, high-precision data support is provided for agricultural application such as crop growth assessment and water resource management, and the method is suitable for popularization and application. The method is suitable for cloudy and rainy areas and various crop types.

The invention provides a regional soil component detection method based on a remote sensing image, and belongs to the technical field of soil detection. Aiming at the problems that a traditional detection method depends on a single optical remote sensing image, is interfered by weather and vegetation, and is low in detection precision, low in efficiency, unguaranteed in data credibility and the like, radar and thermal infrared remote sensing data are introduced to be fused with an optical image, features are extracted through principal component analysis, a generative adversarial network is adopted to carry out hyperspectral image super-resolution reconstruction, and a high-resolution hyperspectral image is obtained. A genetic algorithm is utilized to realize adaptive wave band selection, a real-time atmospheric correction model is constructed, a recurrent neural network and a spatial convolutional neural network are utilized to mine spatio-temporal context information, a quantum computing acceleration algorithm is introduced, block chain management data is utilized, and unmanned aerial vehicle cooperative operation is combined. According to the method, the detection precision, efficiency and data credibility are remarkably improved, and the regional soil component detection process is effectively optimized.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention relates to the technical field of remote sensing, in particular to a method, device and equipment for identifying algal blooms in rivers around lakes and reservoirs and a storage medium. The method comprises the following steps: acquiring sentinel No.2 remote sensing image data at historical moments in a preset area, and performing radiometric calibration, atmospheric correction and super-resolution reconstruction; extracting an algal bloom sample and a non-algal bloom sample; performing feature conversion on the samples to obtain a sample feature data set; performing model training by using an XGBoost method based on the sample feature data set to obtain a water bloom recognition model; and inputting a feature data set obtained by converting the reflectivity image data of the to-be-monitored area into the water bloom recognition model to obtain a water bloom recognition result. According to the method, water bloom monitoring is carried out by adopting sentinel No.2 remote sensing image data, so that the problem that a conventional water color satellite cannot be used for monitoring due to the narrow width of a river channel around a lake and a reservoir in the related technology is solved; and the method does not need to set a water bloom identification threshold value in advance and extract a water body, so that the automation degree of water bloom identification is improved.

The invention provides a vegetation remote sensing monitoring method and system based on a geographic information cloud platform, and the method comprises the steps: determining the time and region of target monitoring, and determining the geographic boundary and time range of target monitoring according to the demands of a user; screening a suitable data source, and cutting the screened remote sensing image; carrying out cloud layer interference processing on the image data by adopting a cloud removal algorithm, and removing pixels covered by a cloud layer; carrying out radiation correction, geometric correction and atmospheric correction standardization preprocessing on the image; calculating a vegetation ecological index, a vegetation drought index and a meteorological factor of the target area based on the preprocessed remote sensing image; and displaying the processed vegetation growth time sequence monitoring data on a user interface in an image form, and supporting exported data of the data for subsequent analysis. According to the method, through an efficient processing algorithm, the precision and timeliness of vegetation growth state monitoring are remarkably improved, and important technical support is provided for vegetation management, ecological protection and scientific research.

The invention discloses a remote sensing image vegetation coverage prediction method and system based on deep learning, and relates to the technical field of vegetation coverage prediction, and the method comprises the steps: collecting multi-source remote sensing image data and a corresponding truth value label, carrying out the preprocessing, and extracting the features of spectrums, textures and the like to construct a data set; constructing a prediction model containing a convolutional neural network (extracting spatial features) and a recurrent neural network (processing time sequence features), and training the model through cross validation and regularization; and finally inputting new data to realize prediction. In the preprocessing, the optical remote sensing image is subjected to radiometric calibration, atmospheric correction and geometric correction, aperture radar image denoising and laser radar data point cloud filtering classification. The system comprises a data collection module, a preprocessing module, a feature extraction module, a model construction module, a training module and a prediction module. According to the scheme, multi-source data and deep learning are fused, the vegetation coverage prediction precision is improved, and technical support is provided for ecological monitoring and the like.

The invention discloses a photovoltaic panel surface pollution degree image evaluation system. The system comprises an image acquisition module, an image processing module, a multi-task evaluation module and a pollution degree determination module. Multi-spectral image data of the surface of a photovoltaic panel are collected, after radiation calibration and atmospheric correction processing, spectral reflectivity features and spatial texture features are extracted and fused, a multi-task deep learning model is input, a pollution type classification result and a power generation efficiency loss weight of each pixel region are output, and a pollution type classification result of each pixel region is obtained. And finally, generating a pollution type spatial distribution diagram, calculating the overall power generation efficiency loss percentage, and determining the pollution degree grade. According to the method, the problem that the mixed pollution type cannot be distinguished and the differentiation influence cannot be evaluated in the prior art is solved, accurate quantitative evaluation of the pollution degree is realized, and a reliable basis is provided for fine operation and maintenance of a photovoltaic power station.

The invention discloses a cloud anomaly detection and removal method based on a DDIM remote sensing image in the technical field of remote sensing image processing, and the method comprises the following steps: S1, carrying out the loading and preprocessing of a data set, obtaining a multispectral remote sensing image, and carrying out the radiation correction and atmospheric correction; s2, a forward diffusion process: converting the cloudless image into a noise image sequence by gradually adding noise; s3, training a U-Net noise prediction network to predict noise in a forward diffusion process and reconstruct an original image; s4, reverse denoising sampling: restoring the noise image into a cloudless image based on the trained noise prediction network and a deterministic sampling strategy; and S5, generating a pixel-level cloud mask, and obtaining a cloud abnormal region through pixel-by-pixel difference of the original cloud image and the reconstructed cloud-free image in combination with an adaptive threshold. According to the method, pixel-level positioning and high-fidelity reconstruction of the cloud layer are realized based on the de-noising diffusion model through a back diffusion sampling and deterministic sampling method, and the effect of the remote sensing image is improved.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention discloses a roadbed settlement and landslide hidden danger identification method and system along a traffic corridor, and the method comprises the steps: obtaining a time sequence satellite-borne data set of a target region, and carrying out the format conversion of the data format of the time sequence satellite-borne data set; generating an interference pair network based on the time baseline threshold; generating a filtered differential interference image set; performing differential interference phase unwrapping processing on the filtered differential interference image set to obtain an unwrapped differential interference image set; performing atmospheric delay phase correction on the unwrapped differential interference image set to obtain an unwrapped interference image set after atmospheric correction; calculating to obtain the annual average settlement rate of the linear traffic corridor area and a time sequence accumulated deformation result; based on the annual average settlement rate of the linear traffic corridor area, the spatial settlement gradient field information of the linear traffic corridor area is extracted, the settlement rate and the spatial settlement gradient field information meeting the hidden danger judgment standard are output, the roadbed settlement and landslide hidden danger extraction and recognition result of the target area is obtained, and the road area detection efficiency is improved.

The invention discloses an ecological environment remote sensing data analysis method, and belongs to the technical field of ecological environment. The data analysis method comprises the following steps: collecting remote sensing image data of a monitored water area, and carrying out atmospheric correction and geometric correction on the remote sensing image data; utilizing a co-occurrence matrix method to extract water body spectral texture features so as to identify spectral texture feature wavebands related to water body turbidity, chlorophyll content and dissolved oxygen; constructing a water quality parameter inversion model, training and verifying the model by using field sampling data, and optimizing the model; regularly acquiring new remote sensing image data of a monitoring area, inputting the new remote sensing image data into the optimized water quality parameter inversion model, and monitoring spatial and temporal distribution changes of water pollution parameters; determining a key area according to an inversion result, and obtaining high-temporal-spatial-resolution supplementary data of the key area by using the unmanned aerial vehicle; and collecting meteorological and hydrological station observation data, and comprehensively analyzing the source and flow direction of pollutants in combination with the inverted water quality parameter spatial and temporal distribution information.

The invention relates to the field of ecological environment monitoring, and discloses a vegetation coverage spatio-temporal change analysis method, which comprises the following steps of: firstly, preprocessing remote sensing image data of a research area, including radiometric calibration, atmospheric correction and image splicing and cutting; then calculating a vegetation index value based on the normalized vegetation index, and extracting a vegetation coverage through a pixel bipartite model; and performing difference calculation on the vegetation coverage data in different periods, analyzing spatial and temporal change characteristics of vegetation coverage, and generating a vegetation coverage grading map and a change distribution map. According to the method, the spatial distribution and dynamic change trend of vegetation coverage can be accurately reflected, the defect that a traditional vegetation monitoring method is sensitive to soil background and atmosphere interference is overcome, the method has the advantages of being high in precision, wide in applicability and high in dynamic monitoring capacity, and a scientific basis is provided for formulation and effect evaluation of regional ecological protection measures.

The invention discloses a deep learning inversion AOD-assisted atmospheric correction-based method and device and a medium, and relates to the field of atmospheric remote sensing and deep learning cross technologies, and the method comprises the steps: constructing a multi-type training sample set fusing actual measurement and physical simulation data, and carrying out mass screening and layering processing to obtain a multi-type training sample set; obtaining a pre-training sample, a fine tuning sample and an extreme scene sample matched with the satellite remote sensing data; establishing a deep learning network model taking data loss and inverse operator constraint loss weighted fusion as a total loss function, and adopting a cross-satellite transfer learning strategy to sequentially complete pre-training, satellite exclusive fine tuning and extreme scene enhancement training to obtain a trained aerosol optical thickness inversion model; radiometric calibration, cloud detection, geometric correction preprocessing and input model reasoning calculation are carried out on satellite remote sensing original data, post-processing and quality grading are carried out on an output result, and an aerosol optical thickness inversion result used for assisting atmospheric correction is obtained.

The invention relates to the technical field of hyperspectral alteration, in particular to a satellite hyperspectral alteration mineral remote sensing quantitative recognition method based on deep learning. The method comprises the following steps: acquiring high-resolution 5B satellite hyperspectral data, DEM data and atmospheric parameter data; performing multi-source noise suppression processing on the high-resolution 5B satellite hyperspectral data to obtain a noise correction data set; performing terrain shadow correction based on the noise correction data set and the DEM data, and performing atmospheric correction in combination with the atmospheric parameter data to obtain a pre-corrected hyperspectral data set; through multi-source data fusion, noise and environment interference correction, key wave band screening and multi-dimensional depth feature extraction and fusion, the quality and characterization capability of hyperspectral data are effectively improved, and a solid data basis is provided for subsequent refined mineral recognition and analysis.

The invention relates to the field of marine atmospheric waveguide environment detection, and discloses a method for predicting a marine atmospheric correction refractive index profile, which comprises the following steps of: acquiring meteorological observation data at different heights; calculating an evaporation waveguide height prediction value and a reference atmosphere correction refractive index profile; constructing a fusion relation model of the critical gradient of the bit refractive index and meteorological observation data; inputting meteorological observation data into the fusion relation model to obtain a critical gradient of a refractive index of a prediction bit, calculating a critical gradient of a refractive index of a reference bit, and obtaining an optimal weight coefficient of the model through iteration by using a particle swarm intelligent optimization algorithm; and inputting meteorological observation data into the optimized fusion relation model, calculating an optimized bit refractive index critical gradient, and finally obtaining an optimized atmosphere correction refractive index profile. According to the method disclosed by the invention, the influence of different meteorological conditions on the critical gradient of the refractive index is considered in the calculation process of the atmospheric correction refractive index profile, and the prediction precision of the atmospheric correction refractive index profile is improved.

The present invention provides a time-series InSAR interseismic deformation interferogram correction and screening method, which belongs to the field of measurement technology and specifically includes: step 1, using the GACOS method to perform atmospheric correction on the original interferogram set, then using the improved CANDIS method to perform perturbation phase correction, and performing line-of-sight plate motion phase correction based on the plate model to obtain a long-term corrected interferogram set; step 2, using the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term corrected interferogram set to obtain a target interferogram set; step 3, based on the target interferogram set, using the intermittent stacking method to set the intermittent coherence pixel number threshold to generate the InSAR average interseismic deformation velocity field. The solution of the present invention improves the accuracy and adaptability of interseismic deformation signal extraction.