327 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 a quality-graded remote sensing image composite cloud removal method, system and device. The method comprises the following steps: preprocessing an acquired remote sensing image; based on the preprocessed data, thin cloud radiation correction is carried out; based on the data after thin cloud radiation correction, thick cloud detection is carried out; based on the data after thick cloud detection, thick cloud missing reconstruction is carried out; performing atmospheric correction based on the data after the thick cloud missing reconstruction; and performing data quality grading based on the data after atmospheric correction. According to the method, a series of standardized processing is realized on data acquired under different space and time conditions, the influence of atmospheric radiation attenuation is eliminated, and the data are formatted into surface reflection data with consistent observation characteristics, so that the utilization rate of remote sensing images is improved, and effective comparison and analysis of subsequent application research are promoted.

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 belongs to the field of remote sensor calibration, and provides an ROLO model improvement method combining foundation and meteorological satellite lunar observation data, which comprises the following steps of: acquiring a meteorological satellite lunar observation image data set and a foundation lunar observation image data set, and respectively correcting the meteorological satellite lunar observation image data set and the foundation lunar observation image data set; obtaining standard meteorological satellite observation moon radiance and foundation observation moon radiance, and calculating observation moon irradiance; calculating geometric parameters of observation points, the earth, the moon and the sun at the observation moments of the foundation and the meteorological satellite, and calculating the moon irradiance of the ROLO model; polynomial fitting is adopted for the relative deviation between the observed moon irradiance of different wave bands and different moon phase angles and the moon irradiance of the ROLO model, and the ROLO model is improved. The method is not affected by the atmosphere, and errors caused by atmospheric correction of ROLO model foundation observation data are directly avoided; and the phase angle dependence of the ROLO model can be corrected, and the prediction precision of the ROLO model can be improved.

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 relates to the technical field of winter wheat growth and development recognition, in particular to a winter wheat growth and development recognition method based on a satellite remote sensing image, and the method comprises the following steps: S1, carrying out the preprocessing of the satellite remote sensing image; according to the method, satellite remote sensing images are preprocessed, including radiation correction, atmospheric correction, geometric correction and the like, so that noise and distortion in the images are eliminated, image quality is improved, planting and phenological characteristics of winter wheat are investigated and analyzed, specific planting phenological characteristics of winter wheat are obtained, extracted characteristics are classified and identified by using a support vector machine (SVM), and the winter wheat planting phenological characteristics are identified. Winter wheat areas in the images are judged, characteristics related to the wheat development stage can be extracted by analyzing the spectral reflectivity of wheat in different growth stages, the growth condition of winter wheat is evaluated, and the health condition of winter wheat and whether the problems of diseases and insect pests, water stress, nutrient deficiency and the like exist or not are analyzed according to the evaluation result. And corresponding management suggestions are provided accordingly.

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 a high-precision spectral remote sensing atmospheric correction and cloud removal processing method fusing multiple time phases, atmospheric parameters and ground reflectivity, and relates to the technical field of computer vision, deep learning image processing and remote sensing. Comprising the following steps: acquiring an atmosphere top reflectance and surface reflectance model of a Sentinel-2 satellite remote sensing image in a Guilin region, and constructing a data set by atmosphere data; constructing a diffusion model for multispectral remote sensing image processing; constructing a condition control and coding structure to carry out multispectral parameter and atmospheric parameter processing; and establishing a unified model to carry out atmospheric correction and cloud removal processing on the spectral satellite remote sensing image. According to the method, multi-temporal multi-spectral parameters, atmospheric aerosol optical thickness, atmospheric water vapor and ground reflectivity remote sensing characteristic parameters and data are fully fused, and a brand new unified model is established to realize atmospheric correction and cloud removal processing of a high-precision spectral satellite remote sensing image; and atmospheric correction and cloud removal processing of the high-precision spectrum satellite remote sensing image are realized.

The embodiment of the invention provides a surface reflectance inversion method and device for optical satellite remote sensing data, and the method comprises the steps: obtaining sensitive parameter data corresponding to pixels, the pixels are all pixels in to-be-inverted optical satellite remote sensing data, and the optical satellite remote sensing data are collected by an optical satellite sensor, the sensitive parameter data comprises observation geometric data, atmosphere auxiliary data and elevation data; inputting the sensitive parameter data into an atmospheric correction neural network model to obtain an atmospheric correction coefficient of the pixel, the atmospheric correction neural network model being obtained by pre-training based on an atmospheric correction coefficient lookup table of the optical satellite sensor; and calculating according to the atmospheric correction coefficient and the radiation brightness of each pixel to obtain the surface reflectance of the optical satellite remote sensing data. According to the method, the inversion of the surface reflectance is carried out pixel by pixel based on the neural network model, and the method has the characteristics of high universality, high inversion precision and high calculation speed.

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.

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.

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 a river flow measuring and calculating method based on multi-source remote sensing data and deep learning, and belongs to the technical field of water resource monitoring. Comprising the following steps: obtaining an original remote sensing image, and carrying out geometric correction and atmospheric correction on the original remote sensing image to obtain a calibrated multispectral image; calculating a normalized difference water body index NDWI by using near infrared and green wave bands, and combining the normalized difference water body index NDWI with the calibrated multispectral image to form a multi-channel input tensor containing RGB, NDWI and elevation information as multi-source input data; the UNet model is improved; generating a water body probability graph by using the multi-source input data through an improved UNet model; and based on the water body probability graph, extracting a feature vector of the river, and predicting the river flow through an XGoos machine learning regression model. According to the invention, the efficiency and accuracy of water resource monitoring and management can be improved.

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 provides a hyperspectral image dominant tree species identification method based on deep learning, and the method comprises the following steps: 1), obtaining data: obtaining the hyperspectral image data of trees in a forest region through a hyperspectral image sensor carried by an unmanned plane; 2) data preprocessing: carrying out preprocessing operations such as geometric correction and atmospheric correction on the obtained hyperspectral image data, and carrying out tree feature extraction on the denoised data; and 3) data set construction: calibrating the extracted features to make a tree species identification data set, and increasing the number of training samples by adopting a data amplification method. And 4) training the model: training the training set by using the built DeepLab v3 + model to obtain a trained model. And 5) model testing: detecting and identifying the test set picture by using the generated model.

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 provides an atmospheric correction method and device for a solar zenith angle lower coastal water area radiation signal. The method provided by the invention comprises the following steps: generating noon remote sensing reflectivity satellite data based on observation data of a coastal water area by utilizing an ultraviolet-near infrared atmospheric correction combination model; utilizing a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data based on the spatial resolution and the time resolution of the data; calculating a Rayleigh correction radiance based on a Rayleigh scattering lookup table corresponding to the observation data, and matching the Rayleigh correction radiance with the adjusted noon remote sensing reflectivity satellite data based on time information and position information to obtain a training set; and inputting the to-be-corrected coastal water area radiation signal into the model trained based on the training set, and determining the off-water radiance of the radiation signal. According to the method and the device provided by the invention, atmospheric correction of a coastal water area radiation signal at a high solar zenith angle is realized for a turbid coastal water area.