214 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 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 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 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 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.

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 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 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 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 provides a water quality parameter inversion method and system based on spectrum correction and electronic equipment, and relates to the technical field of water environment remote sensing. According to the method, the water quality parameter and the water body reflectivity value of the surface feature spectrometer are measured synchronously when the remote sensing image passes through the scene, spectrum correction is carried out on the remote sensing image by using actually measured data as a reference, and the accuracy of water quality parameter inversion is improved. Firstly, water quality parameter and water body reflectivity value measurement of a surface feature spectrometer are synchronously carried out innovatively when a remote sensing image passes through a scene, so that the consistency of measured data and the remote sensing image in time and space is ensured, and a reliable basis is provided for subsequent spectrum correction and water quality parameter inversion modeling. Secondly, a spectrum correction model is established based on the synchronously acquired measured data, the spectrum of the remote sensing image can be adjusted according to the measured data, and the reflectivity deviation caused by atmospheric correction errors and sensor calibration errors is reduced, so that the accuracy of the spectrum data is improved.

The invention relates to the technical field of mine slope deformation monitoring, in particular to a mine slope absolute displacement field monitoring and error self-correcting method and system. The method comprises the following steps: according to Kriging synergetics interpolation and adaptive weighted fusion, processing GB-SAR relative displacement, a projection coefficient matrix and a motion mode partition after atmospheric correction to obtain an absolute displacement field calibrated by a GNSS (Global Navigation Satellite System); processing the displacement period characteristic parameters and the area displacement gradient field by utilizing multi-characteristic fuzzy comprehensive evaluation to obtain a slope local stability comprehensive index field; and high-precision absolute displacement field monitoring and error self-correction are completed by combining a GNSS early warning level, an absolute displacement field calibrated by the GNSS and a local stability comprehensive index and utilizing graded early warning and displacement field traceability analysis. According to the invention, high-precision planar absolute displacement field monitoring, accurate displacement abnormal cause identification and error self-correction can be realized.

The invention discloses an active and passive fusion water depth inversion method based on adaptive spectrum weighting, and the method comprises the steps: carrying out the atmospheric correction of a multispectral satellite image, extracting a target region, and removing a land region and a deepwater region; the method comprises the following steps: acquiring a seawater point cloud of a satellite-borne photon counting laser radar in a target area, extracting sea surface and seabed information, and performing refractive index correction and tide correction to obtain a reference water depth; matching each pixel in the multispectral satellite image with a reference water depth, and dividing the multispectral satellite image into a reference pixel and a to-be-measured pixel; according to the spectral value of the multispectral satellite image, the Euclidean distance between each to-be-measured pixel and all reference pixels is calculated, and the reciprocal of the distance is used as the weight of each reference pixel; and introducing a weight-to-weighted least square method, and fitting a relationship from the spectral value to the water depth for each pixel to be measured to realize water depth inversion of the pixels to be measured. The method can effectively improve the active and passive fusion water depth inversion precision when the water depth distribution is uneven and the shallow sea area environment is complex.

This disclosure relates to machine learning models for performing atmospheric correction on an input image. To train a machine learning model to perform atmospheric correction on an input image comprising atmospheric distortion, a processor applies a first trained machine learning model to a training image to determine a first output image, the first trained machine learning model being configured to perform atmospheric correction on an input image comprising atmospheric distortion. The processor applies a second machine learning model to the training image to determine a second output image, wherein the second machine learning model has a smaller model architecture than the first trained machine learning model. The processor trains the second machine learning model by minimising a loss based on the first output image and the second output image to determine a second trained machine learning model.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention provides a water color remote sensing parameter inversion-oriented hyperspectral data quality evaluation method, which relates to the technical field of optical remote sensing and comprises the following steps of: dividing a plurality of hydrological response unit sub-graphs from a hyperspectral remote sensing image of a target area by taking a preset multi-scale biological optical fingerprint wave band as a reference; performing water color noise transmission analysis on the plurality of hydrological response unit sub-graphs, and outputting a plurality of groups of water color inversion signal-to-noise ratio fingerprints; performing atmospheric correction distortion field diagnosis on the hyperspectral remote sensing image, and outputting a near-infrared negative distortion field and a spectral fidelity error field; dynamically fusing to generate a real-time quality entropy field; and confidence domain convergence mapping of the real-time quality entropy field is carried out, and a water color parameter inversion uncertainty thermodynamic diagram is output. The technical problems that in the prior art, a targeted diagnosis mechanism for distortion in the atmospheric correction process is lacked, systematic interference of atmospheric correction on water color parameter inversion precision cannot be captured, and consequently a final water quality monitoring result is unreliable and errors are large are solved.

The invention discloses a remote sensing data-based cultivated land resource dynamic monitoring and checking method and system, and the method comprises the steps: introducing atmospheric scattering parameter estimation in an atmospheric correction process, carrying out the refined estimation of the atmospheric path radiation through combining with the water vapor content and aerosol concentration, and optimizing the atmospheric correction parameters, thereby achieving the dynamic monitoring and checking of cultivated land resources. And the interference of the atmospheric effect on the surface reflectance is effectively weakened. Spectral response function matching and spectral feature standardization processing are implemented, and a spectral difference correction model is constructed, so that the spectral features of the multi-temporal images tend to be consistent. The spectral deviation caused by atmospheric condition fluctuation is remarkably reduced, and the comparability of spectral response between images is improved, so that the accuracy and the stability of cultivated land change detection are improved. And finally, change feature extraction and region identification are performed on the basis of spectrum consistency enhancement, so that the actual change condition of cultivated land resources can be reflected more truly, misjudgment caused by atmospheric noise is reduced, and the reliability of a dynamic monitoring result is enhanced.

The application discloses a FY satellite spectral imager atmospheric correction method and device based on 6S radiation transmission mode, medium and product, and relates to the field of atmospheric correction. The method comprises the following steps: determining the threshold range of a first parameter according to MERSI observation data and determining the threshold range of a second parameter according to atmospheric reanalysis data; generating a target coefficient by applying a 6S radiation transmission mode and a spectral response function of MERSI; constructing a lookup table; obtaining matching data values of the second parameter corresponding to each pixel in target MERSI observation data by applying an interpolation algorithm; determining the target coefficient of each pixel in the target MERSI observation data from the lookup table; obtaining the corrected reflectivity of each pixel by applying the 6S radiation transmission mode; and correcting the true color quick view of the visible light channel according to the corrected reflectivity of each pixel to obtain a corrected true color composite image. The application can perform atmospheric correction on MERSI wide-width global images.

The invention discloses an ultra-long distance cable fault monitoring method and system, and belongs to the field of power system safety monitoring, and the method comprises the steps: obtaining a partial discharge high-frequency transient signal, a hyperspectral remote sensing image and atmospheric parameter data of a cable, carrying out the phase-space reconstruction of the partial discharge high-frequency transient signal, and constructing a chaotic feature; and performing atmospheric correction on the hyperspectral image based on the atmospheric parameters, generating a surface reflectance image, performing spectral unmixing on the surface reflectance image, and extracting a spectral fingerprint. The method comprises the following steps: performing space-time alignment on a partial discharge high-frequency transient signal and a hyperspectral remote sensing image to generate a space-time mapping index, and according to the space-time mapping index, pairing a chaotic feature with a spectral fingerprint to form a space-time feature pair; and predicting the fault probability of each position point of the cable in a future time range through the cross-modal fusion model, and judging a latent fault according to a threshold value. According to the invention, the problem of inaccurate monitoring caused by weak signals and index lag in the prior art can be solved.

The invention provides an offshore area atmosphere and ocean parameter collaborative inversion method and system, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining to-be-inverted parameters which comprise an atmosphere parameter and an ocean parameter, calling a multi-parameter physical information neural network model to carry out the ocean water-leaving radiation calculation of the ocean parameter, and calculating the ocean water-leaving radiation of the ocean parameter; an atmospheric aerosol model is called to carry out parameterization calculation on atmospheric parameters, and scattering parameters and absorption coefficients of atmospheric aerosol are obtained; inputting the ocean water-leaving radiation quantity and the scattering parameter and the absorption coefficient of the atmospheric aerosol into a forward radiation transfer model, and carrying out radiation transfer calculation to obtain entrance pupil radiation brightness of the top of the atmospheric layer; and finally, performing parameter inversion by combining a satellite measurement radiation signal to obtain an inversion parameter corresponding to the to-be-inverted parameter. According to the invention, problems existing in an atmospheric correction algorithm for ocean water color remote sensing in the prior art are solved.

The invention relates to the technical field of ocean remote sensing, in particular to an offshore water quality remote sensing inversion and classification method based on small sample deep learning, which comprises the following steps: acquiring offshore in-situ observation, satellite remote sensing and ocean reanalysis data; carrying out atmospheric correction, carrying out space-time reconstruction on the missing remote sensing reflectivity by adopting a data interpolation empirical orthogonal function, carrying out mathematical transformation on input features, and screening out an optimal feature subset by adopting a strategy based on an average absolute percentage error; constructing a deep learning network based on a generative small sample to invert the offshore soluble inorganic nitrogen and inorganic phosphorus concentration, and determining the water quality grade; and analyzing an inversion mechanism by using an SHAP method. According to the method, the problem of data space-time discontinuity is solved through the data interpolation empirical orthogonal function, the capture capability and inversion precision of the nonlinear relation under the small sample condition are remarkably improved by utilizing the generative network, the model interpretability is realized in combination with SHAP analysis, and scientific support is provided for offshore water quality fine management.

The invention provides a carbon monoxide plume identification and quantification method based on a domestic hyperspectral satellite, and the method comprises the steps: carrying out the radiometric calibration and atmospheric correction of the data of a visible short-wave infrared hyperspectral camera AHS I carried by the domestic hyperspectral observation satellite (GF-5B, Gaofen-5 B satellite); generating a carbon monoxide unit absorption spectrum by using a medium-resolution atmospheric transmission model MODTRAN; inverting a carbon monoxide enhancement value by adopting a matched filtering method; generating a binary plume mask through threshold segmentation; and constructing a deep learning model based on a residual neural network ResNet network, and realizing plume pixel-level identification and carbon monoxide column concentration enhancement value prediction. The method can identify the carbon monoxide plume with high precision and high efficiency, and is suitable for atmospheric pollution monitoring and pollution source quantification.

The invention discloses a coastal zone human activity interference remote sensing quantification and ecological profit and loss evaluation method and device, belongs to the technical field of remote sensing monitoring, and solves the problems of low efficiency due to dependence on field investigation and insufficient reliability caused by separation of ecological evaluation and remote sensing data in the prior art. The method is characterized by comprising the following steps: acquiring two-stage multispectral remote sensing images containing multiple wavebands such as blue light at an interval of 5-10 years and with spatial resolution of 10-30m, and synchronously integrating tide, elevation and geological background data; eliminating environmental errors of image radiometric calibration and atmospheric correction; calculating a normalized building index based on the short-wave and near-infrared band reflectivity, and detecting a quantized interference area in combination with land utilization classification change; and the change area and the corrected intensity change value are extracted and input into an ecological system service value model, and an evaluation result is output by using a unit area value equivalent factor method. According to the method, full-process remote sensing automatic processing is realized, field investigation is avoided, the difference adjustment cost is effectively reduced, and the pertinence of management decision is remarkably improved.