187 results about "Radiometric calibration" patented technology

The invention discloses an intelligent rock weathering detection method based on a multispectral imaging technology, and particularly relates to the field of rock detection.The method comprises the steps that a multispectral imaging device is used for collecting a rock sample image, and then radiometric calibration, geometric correction and image enhancement processing are sequentially conducted on the image. The method comprises the following steps of: preprocessing an image, acquiring spectrum, texture and morphological characteristic parameters from the preprocessed image, collecting samples through a multi-source database, constructing a deep convolutional neural network model, inputting the acquired characteristic parameters into the model to obtain a rock weathering probability vector, judging the weathering degree according to a maximum probability criterion, and outputting a result to a user side. According to the method, multispectral imaging and deep learning technologies are fused, the defects of a traditional detection method are overcome, the rock weathering degree can be detected more accurately and efficiently, nondestructive detection and real-time monitoring are achieved, and the method has important application value in the fields of geological research, engineering construction and the like.

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

The invention relates to an optical satellite absolute radiometric calibration method, device and equipment under ocean low brightness based on an offshore pseudo-invariant field and a medium, and belongs to the technical field of in-orbit absolute radiometric calibration. According to the method, a marine pseudo-invariant field with stable water and atmospheric optical characteristics is determined according to marine observation data of a calibrated optical satellite, the off-water radiance of the marine pseudo-invariant field is calculated according to the chlorophyll a concentration of the marine pseudo-invariant field, the zenith radiance is theoretically calculated, a data source is provided for absolute radiometric calibration of a satellite to be calibrated, and calibration is completed. The method explores the potential of the marine pseudo-invariant field in supplementing the traditional radiometric calibration method, gets rid of the limitation of a fixed and single marine optical buoy MOBY site data source, improves the data volume, the coverage area and the acquisition frequency of effective calibration data, provides guarantee for the continuity of an optical satellite observation task, and has a wide application prospect. And a plurality of calibration sources can be utilized to realize rapid and accurate calibration of the optical satellite, so that the calibration efficiency of the offshore optical satellite is remarkably improved.

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

The invention discloses 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 present disclosure provides a curve-based radiometric calibration method and system of a spaceborne hyperspectral imager, which belongs to the field of remote-sensing optical technologies. A new radiometric calibration coefficient curve is introduced to describe the radiometric properties of sensor response, providing the radiometric calibration coefficients of all bands of the hyperspectral camera with linear variable filter (LVF) within an imaging spectral range to match the implementation of the programmable band selection imaging technology, and thereby efficiently and simply implementing single-band imaging, integral imaging of adjacent band, imaging of randomly-selected band combination and within-spectral-range cyclic imaging. In the present disclosure, the radiometric calibration coefficient is used to cover the entire imaging spectral range of the hyperspectral camera and match the implementation of the programmable band selection imaging technology, and realizing on-orbit absolute radiometric calibration with simple flow and strong universality.

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 discloses an infrared calibration temperature measurement method based on a multi-component insulating gas multispectral imaging device, belongs to the technical field of electric power safety detection, and solves the problem of how to improve the temperature measurement precision of electric power equipment. The non-uniformity correction calibrates the response and bias of each pixel to the same numerical value, the temperature correction eliminates the influence of the lens temperature and the movement temperature on the received electric signal, the shading shutter correction ensures the accuracy and consistency of the shutter or the shading system, the radiation calibration is adopted to convert the measured blackbody voltage value into the temperature value, and the temperature value is converted into the temperature value. The real temperature of the surface of the detected electrical equipment is calculated by combining the temperature value after radiometric calibration conversion, the influence of the atmospheric environment on temperature measurement is eliminated, and the temperature measurement function of the electrical equipment based on an infrared multispectral imaging detector for insulating gas leakage in a complex environment can be realized. And a foundation is laid for further forming portable power multifunctional monitoring.

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 relates to the field of infrared radiation measurement, in particular to a response drift correction device and method for a large thermal inertia infrared system, and the method comprises the steps: respectively arranging a temperature sensor on each optical structure in the large thermal inertia infrared system; placing the large thermal inertia infrared system in different environment temperatures, and carrying out radiometric calibration measurement on the large thermal inertia infrared system by using a calibration black body; temperature values measured by the temperature sensor at different environment temperatures form a temperature vector; calculating a variance expansion factor of each temperature vector, and selecting the temperature vectors meeting conditions as a characteristic temperature subset representing stray radiation in the infrared system; constructing an infrared system response model based on the characteristic temperature subset, and performing multivariate linear fitting on the infrared system response model based on data measured by radiometric calibration to obtain a model coefficient; and performing response drift correction on the large thermal inertia infrared system by using the corrected infrared system response model. The large thermal inertia infrared system response drift correction method can realize large thermal inertia infrared system response drift correction under a wide temperature range.

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 provides a large mountain photovoltaic environment fire danger identification method based on a meteorological satellite, and the method comprises the steps: carrying out the radiometric calibration processing based on the Himawari-8 data of a detection region, obtaining the spectral reflectivity or spectral radiation brightness of each wave band of the detection region, and obtaining a cloud shielding pixel through a cloud detection method; a normalized brightness temperature time sequence change function is established based on the time sequence change of the background brightness temperature, a brightness temperature estimation value is estimated, the maximum assumed threshold value of each stage is determined according to the brightness temperature time sequence change function, and then a time sequence method is used for distinguishing fire point pixels. And finally, carrying out credibility grading on the extracted fire point pixels to obtain a fire point grading graph, and sending out corresponding early warning according to fire point grades.

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

The invention 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 an expressway ponding recognition method based on remote sensing data and deep learning. The expressway ponding recognition method comprises the following steps: collecting single-view complex image data in a synthetic aperture radar; carrying out denoising on the single-view complex image data, removing random speckle noise, and then carrying out smooth processing, geometric correction and radiometric calibration; geocoding is carried out in combination with DEM data, and imaging geometric distortion and position offset are removed; combining the remote sensing image, labeling a data label, and constructing a data set; constructing an image segmentation model based on a U-Net network; inputting an enhanced data set into the image segmentation model to carry out model training; and inputting a radar image needing ponding identification into the image segmentation model to carry out ponding identification in the expressway road domain range. The water body classification method based on the U-Net network is constructed and verified. By making a high-quality urban water body annotation data set and introducing various data enhancement strategies, the segmentation performance of the model in a complex remote sensing image is remarkably improved.

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

The invention discloses an SAR sea surface rainfall detection method based on deep learning, and relates to the technical field of satellite remote sensing image processing. SAR images are screened by matching with GPM rainfall data, preprocessing such as geographical calibration, radiometric calibration, down-sampling and land masking is carried out on the images, the images are cut to obtain a training set, and enhancement is carried out after labeling and data set division. And inputting the training set into a target detection model based on a convolutional neural network or Transform for training, increasing a multi-scale detection head and an attention mechanism module to improve the detection precision, and finally inputting a to-be-detected SAR image into the model and outputting a rainfall area. The precision, the automation degree and the real-time performance of sea surface rainfall area detection can be improved, and the defects of a traditional method are overcome.

The invention discloses an SAR radiation cross calibration method based on an urban wide strong scattering reference point target, and the method comprises the steps: analyzing a meta-file of to-be-calibrated SAR data, extracting corresponding system parameter information and imaging region information, selecting multi-scene reference SAR data from calibrated historical SAR data, and extracting an absolute calibration constant; cutting and registering an area which coincides with the imaging area of the reference SAR data and the to-be-calibrated SAR data and is located in an urban area to make an SSDH data set; carrying out one-round screening and two-round screening on the SSDH data set based on a standard strong scattering point model, and extracting a radiation-stable high-value reference point target appearing in a full time domain; rCS data of the reference point target in the to-be-calibrated SAR data and RCS data of the reference point target in the reference SAR data are calculated; and comparing the RCS data of the same reference point target in the to-be-calibrated SAR data with the RCS data of the calibrated reference SAR data, and performing cross calibration to obtain an absolute radiometric calibration constant of the to-be-calibrated SAR data. According to the invention, low-cost and normalized SAR radiometric calibration processing is realized.

The invention relates to the technical field of spectrograph calibration, in particular to a spectrograph spectrum calibration and radiometric calibration method. According to the technical scheme, the method comprises spectrum calibration and radiometric calibration, the spectrum calibration comprises the steps that firstly, a standard light source is adopted for conducting initial spectrum calibration on an uncalibrated spectrometer to obtain an initial spectrum calibration equation, and then optical differential thickness data is obtained through a sample gas measurement experiment and combined with standard gas absorption cross section data; and calculating a wavelength difference value to correct the initial spectrum calibration equation to obtain a final spectrum calibration equation. The wavelength absolute error precision after spectrum calibration is improved by nearly one order of magnitude, the radiometric calibration error is greatly reduced, the stability and accuracy of radiometric calibration are remarkably improved, the problems that a traditional calibration method is limited in spectral line, poor in light source stability and large in extrapolation error are effectively solved, and the method is suitable for popularization and application. And a solid guarantee is provided for high-precision application of the spectrograph in the fields of environmental monitoring, material science, remote sensing technology and the like.

The invention relates to the technical field of flower moisture determination, and discloses a flower moisture determination system and method based on image data, the flower moisture determination system based on image data comprises the following steps: radiometric calibration is carried out to obtain reflectivity; generating a smooth logarithmic spectrum; calculating a discrete second-order curvature; calculating a curvature residual error; generating a logarithmic spectrum water absorption curvature residual error; generating a single pixel driving quantity; generating a single-value convergence quantity; and generating and outputting a single-value moisture estimation result. According to the method, multiplicative envelope of thin film transmission is converted into additive terms through logarithmic spectrum conversion, noise is suppressed in combination with second-order regular smoothing, non-moisture interference caused by sleeve wrinkle reflection is stripped through discrete second-order curvature calculation and curvature residual extraction, and multi-moisture absorption band characteristics are subjected to adaptive weighted fusion, so that the multi-moisture absorption band characteristics are obtained. Meanwhile, a soft weight field is used for replacing traditional hard segmentation of the flower target area, and result fluctuation caused by boundary drift of the flower target area is avoided; and the automatic online detection rhythm of the flower production line is adapted.

The invention discloses a wetland human interference activity rapid identification method based on remote sensing and deep learning, and relates to the technical field of image identification, and the method comprises the steps: 1, carrying out the radiometric calibration, geometric registration and pixel-level alignment of a multispectral remote sensing image of a target wetland obtained at different time phases, and dividing the multispectral remote sensing image into image tile units according to a preset size; 2, two image tile units of different time phases at the same geographic position are paired to form a tile pair, and after fusion of a spectral coding branch and a time coding branch, a change coding graph is output and used for representing wetland surface change characteristics; step 3, constructing a double-layer adjacent pixel graph by taking the change coding graph as input, and performing segmentation under multiple scales through a progressive form graph segmentation algorithm to obtain communicated change clusters; and 4, comparing the attribute values of the change clusters with a template threshold value, and carrying out type interference activity type judgment on each change cluster. The method has rapid processing capability and good adaptability.

The invention relates to the technical field of territorial space planning, and discloses a territorial space planning environmental influence monitoring method, which comprises the following steps of: performing radiometric calibration and atmospheric correction on an original remote sensing image, converting the original remote sensing image into surface reflectance, screening a heavy metal sensitive wave band, eliminating scattering noise and estimating an inter-wave band noise covariance matrix; constructing an end member spectrum library, optimizing end members through non-negative matrix factorization, inverting abundance by using an alternating direction multiplier algorithm and meeting physical constraints, and calculating covariance of unmixing residual errors; and designing a deep neural network model, and inputting abundance and reflectivity characteristics. According to the method, the timeliness of pollution diffusion early warning is improved through construction of a multi-source data collaborative treatment platform, dynamic tracking of a pollution migration path, an intelligent prediction model and a real-time calculation algorithm, the spectrum interference bottleneck of multi-metal combined pollution is broken through through a spectrum analysis method, and decision support is provided for territorial space environment treatment.

The invention discloses a physical constraint deep learning forest biomass estimation method and system. The method specifically comprises the steps of satellite-borne laser radar GEDI footprint preprocessing and quality screening; sAR radiometric calibration, terrain correction and speckle noise filtering are carried out; optical data radiation correction, atmospheric correction and geometric correction; multi-source remote sensing data space-time alignment and feature variable extraction; constructing a deep learning model fused with physical constraints; feature optimization and model reconstruction are carried out based on space-time interpretability analysis (SHAP); performing comparison and precision verification on the reconstructed final model; and generating a high-resolution annual forest biomass map based on the optimal deep learning model. The estimation result obtained by the method provided by the invention has relatively high precision and accuracy, and scientific data support and decision suggestions are provided for regional forest resource management and carbon sink capability evaluation.

The invention relates to a soil organic carbon spatial distribution prediction method and system based on multispectral satellite remote sensing data, and the method comprises the steps: collecting multispectral satellite remote sensing image data, carrying out the geometric correction and radiometric calibration, extracting a normalized difference vegetation index and an earth surface humidity index, and determining an earth surface heterogeneity distribution diagram; dividing a high-heterogeneous region and a low-heterogeneous region, obtaining a complex surface environment partition map, carrying out soil organic carbon preliminary prediction by adopting a random forest algorithm, and obtaining preliminary continuous distribution data; extracting a local change gradient, inputting the local change gradient into a convolutional neural network, and carrying out spatial convolution operation to obtain fine continuous distribution data; according to the fine continuous distribution data, dynamically adjusting the grid size according to the local change gradient by adopting a self-adaptive grid division method, and determining discrete grid units; and outputting a final soil organic carbon spatial distribution prediction result based on the fine continuous distribution data in the discrete grid units. According to the invention, accurate prediction of soil organic carbon spatial distribution in a complex earth surface environment is realized.

The invention provides a garden carbon sink function zoning method fusing multi-source remote sensing data, and relates to the technical field of garden carbon sink evaluation and remote sensing monitoring. The method comprises the following steps: acquiring multi-source remote sensing data such as optical remote sensing, radar remote sensing and laser radar; performing radiometric calibration, atmospheric correction and geometric correction preprocessing on the data; vegetation characteristic parameters such as a normalized vegetation index, a leaf area index and surface temperature are extracted; calculating garden carbon sink efficiency parameters based on the vegetation characteristic parameters; calculating garden carbon sink function zoning parameters in combination with topographic data; performing carbon sink function zoning on the garden area by adopting a clustering algorithm, and dividing high, medium and low carbon sink function level areas; and outputting a carbon sink function division spatial distribution diagram and a data report. According to the method, evaluation comprehensiveness is improved through multi-source data fusion, accurate quantification is realized through innovative parameter calculation, scientificity and repeatability are improved through an objective partitioning method, and garden carbon sink fine management is effectively supported.

The invention discloses a remote sensing load ultra-large dynamic range radiometric calibration device and method, and relates to the field of radiometric calibration, and the device comprises a driving module, and a light source, a diffuse reflection plate structure, a two-dimensional scanning mirror, an aperture diaphragm, a shutter and a detector which are sequentially arranged according to a light path transmission direction; the two-dimensional scanning mirror, the aperture diaphragm, the shutter and the detector are all connected with the driving module; light emitted by the light source passes through the diffuse reflection plate structure and the two-dimensional scanning mirror and then is subjected to first-time radiation quantity value attenuation, and passes through the aperture diaphragm and then is subjected to second-time radiation quantity value attenuation; the driving module is used for driving the two-dimensional scanning mirror, the aperture diaphragm, the shutter and the detector; the shutter is arranged at the front end of the photosensitive area of the detector; the shutter and the detector are used for detecting the light radiation quantity after two times of radiation quantity value attenuation. According to the invention, the remote sensing load can be subjected to radiometric calibration by using an intense radiometric calibration source such as the sun to obtain the same radiation reference as other in-orbit remote sensing loads, so that the reference unification of remote sensing data is ensured.

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

The invention provides a movable baffle plate for radiometric calibration in vacuum circular mould equipment, which is provided with a base, a movable baffle plate module, a driving guide module, a limiting module and a control module, the base is fixed on a bottom test platform of space environment simulation test equipment, and the movable baffle plate module comprises a framework and an expansion plate heat sink, the framework is used for providing support for the expansion plate heat sink, a rack is arranged below the framework, rolling wheels are symmetrically arranged on the two sides of the rack, the driving guide module is provided with a vacuum motor, a commutator and a gear, the vacuum motor provides power for the gear, and the gear is matched with the rack on the framework. The technical problem that the accuracy of a radiometric calibration result is reduced due to the fact that an existing large spaceflight device or an on-orbit camera is easily affected by blackbody temperature rise radiation when carrying out radiometric calibration is solved. The device can be widely applied to vacuum circular mould equipment tests.