151 results about "Radiometric calibration" patented technology

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.

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 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 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 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 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.

The invention discloses a crop growth period monitoring method and system based on an AI algorithm model, and belongs to the technical field of agricultural information, and the method specifically comprises the steps: collecting the weather, growth cycle and soil data of a target crop in a source place and an introduction place; shooting images of the target crop at different growth stages by using an unmanned aerial vehicle, and performing feature marking; by loading and training a target detection model, identifying a growth stage of the crop; inputting real-time invisible light wave band single-channel radiometric calibration image data into the target detection model to obtain growth state and growth period information of crops; combining the introduction environment and the basic growth data, utilizing a time sequence analysis algorithm to predict the growth period of the crops, analyzing the prediction result, comparing the difference between the origin and the introduction site, and adjusting agricultural management measures to adapt to the influence of environmental conditions and genetic factors on the growth period of the crops.

This invention relates to a protective device for a low-temperature blackbody radiation source, belonging to the field of low-temperature radiation metrology and calibration technology. It solves the problem in existing technologies where the temperature inside the blackbody cavity is easily affected by the external environment when calibrating infrared instruments and equipment using a low-temperature blackbody radiation source, leading to temperature instability within the blackbody cavity. The invention includes a protective body, a piping assembly, and a cap. The protective body has a protective cavity extending through it, with one end connected to the opening of the blackbody cavity and the other end serving as a measuring end. The cap is positioned at the measuring end of the protective cavity. The protective cavity is connected to an external dry nitrogen source via the piping assembly, which supplies both low-temperature and room-temperature dry nitrogen to the protective cavity, isolating the blackbody cavity from the external environment. This invention effectively isolates the blackbody cavity from the external environment, preventing external environmental influences on the temperature inside the blackbody cavity and maintaining its stability.

The invention provides a satellite-borne SAR radiometric calibration precision evaluation method and system, a medium and equipment, and belongs to the technical field of radiometric calibration, and the method comprises the steps: recognizing and analyzing an error source which affects the calibration precision; selecting a flat area as a calibration field according to the error source; arranging a plurality of standard calibration devices with known radar cross sections in an imaging band according to the satellite top crossing position and the wave position; controlling the satellite-borne SAR to irradiate the selected area at a specified wave position, acquiring and processing scene echo data, and generating an SAR image; according to the SAR image and a known radar cross section, calculating a radar cross section measurement value; and evaluating the satellite-borne SAR radiometric calibration precision according to the measured value of the scattering sectional area and the true value of the scattering sectional area. According to the method, multiple types of error sources in the radiometric calibration link are identified and analyzed, the calibration field is selected and multiple standard calibration devices are arranged based on the error sources, and the actually measured data are compared with the true value, so that the accuracy and credibility of radiometric calibration precision evaluation are improved.

The invention discloses a high-precision calibration method of a hyperspectral laser radar for photon counting, which belongs to the technical field of laser radars and comprises a spectrum calibration stage and a radiation calibration stage. Spectral calibration is used for extracting spectral response parameters of each channel based on wavelength-by-wavelength scanning of a monochromator and synchronous measurement of a beam splitter; a detection link is strictly decoupled through radiometric calibration, the effective attenuation coefficient of a water body is extracted in situ through a slope method, full-link radiation correction is carried out in combination with an air whiteboard reference and window transmittance, and the target intrinsic spectral reflectivity without interference is inverted. The method can be used for data correction, target accurate classification and identification and high-fidelity quantitative detection of the hyperspectral laser radar for photon counting, and solves the problem of serious spectral distortion of an original signal caused by non-uniform light source energy of the hyperspectral laser radar and complex scattering absorption of a water body.

The invention belongs to the technical field of deep space exploration, and particularly relates to a multispectral image displacement depth adaptive mapping method and system based on radiometric calibration, and the method comprises the steps: obtaining original black field data with the input bit depth being N and original flat field data with the input bit depth being N; after bad pixels are shielded according to the original black field data and the original flat field data, row offset and row response are calculated; sequentially determining a global scale according to the row offset and the row response, calculating an integration slope, and generating a lookup table according to the integration slope; and acquiring real-time monitoring input, reading the M-bit output value corresponding to the real-time monitoring input according to the lookup table, and correcting and updating the lookup table according to the M-bit output value. Based on a parameterized calculation process (derivation from row bias and row response to a global scale and an integrated slope), different input bit depth and output bit depth requirements can be met by modifying input parameters, inherent limitation caused by hard coding is avoided, and the configurability of cross-platform tasks is improved.

The invention discloses a radiance parameter design method of an integrating sphere, relates to the field of functional design of integrating sphere products for radiometric calibration of space remote sensing cameras, and solves the problems of repetition, omission, environmental change influence, subjective judgment difference and the like due to the fact that radiance parameters of an existing integrating sphere depend on a large number of manual experiments and exploration. The method comprises the following steps of: deducing all different radiation brightness values which can be output by the integrating sphere according to the total quantity of lamps in the integrating sphere and the spatial position distribution of the lamps and a fixed rule, and establishing a lamp control logic table for the radiation brightness values and the on-off relationship of the lamps in the corresponding integrating sphere; and program control software is designed according to the lamp control logic table to perform automatic radiation brightness acquisition, the acquired radiation brightness values are subjected to de-duplication and then are rearranged, an optimized lamp control logic and radiation brightness value mapping table is obtained, and the design of the radiation brightness parameters of the integrating sphere is realized. According to the method, the influence on the radiation brightness value precision caused by omission, experimental environment interference and the like of an artificial dominant experiment is completely avoided.

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

The present invention provides a cross-calibration method, system, electronic device, and medium for geostationary satellites, belonging to the field of remote sensing technology. The cross-calibration method includes: obtaining data to be calibrated of the satellite to be calibrated and calibration source data of a reference satellite; performing equal longitude and latitude projection on the data to be calibrated and the calibration source data to obtain projection data to be calibrated and calibration source projection data; performing spatiotemporal matching on the projection data to be calibrated and the calibration source projection data to obtain matching data to be calibrated and calibration source matching data; filtering the DN values ​​in the matching data to be calibrated and the radiance values ​​in the calibration source matching data to obtain a sample data set; and fitting samples in the sample data set to determine a radiometric calibration coefficient. Accurate cross-calibration is achieved for satellites that do not conform to international standards, such as the Indian geostationary satellite INSAT-3DR.

This invention discloses a remote sensing classification method and device for periglacial landforms. The method includes: establishing remote sensing classification rules for periglacial landforms, dividing periglacial landforms into primary and secondary periglacial landform types; collecting remote sensing images and digital elevation model data of the target area in permafrost regions at various resolutions, and performing radiometric calibration and atmospheric correction on the remote sensing images; performing first-scale segmentation and second-scale segmentation on the processed data to generate two-level image object layers, with the first-scale segmentation generating a primary image object layer corresponding to the primary periglacial landform type and the second-scale segmentation generating a secondary image object layer corresponding to the secondary periglacial landform type; extracting and analyzing features for each image object in the primary and secondary image object layers; and, based on the remote sensing classification rules for periglacial landforms, classifying the image objects in the primary and secondary image object layers into the corresponding primary and secondary periglacial landform types, respectively, and outputting a remote sensing classification map of periglacial landforms.

The invention relates to the technical field of electronic information, in particular to a recursive three-dimensional phase unwrapping method, which comprises the following steps of: initializing, performing radiometric calibration and registration on a multi-temporal SAR (Synthetic Aperture Radar) image, calculating NAD and screening coherent scatterers, executing static three-dimensional unwrapping and filing parameters; recursive unwrapping: adaptively determining an NAD threshold value, distinguishing updated / newly added candidates, performing time smooth recursion and space iteration on an updated set, eliminating anomalies by space-time consistency, and performing static unwrapping and grid connection on a newly added set; initialization updating is carried out, Kalman recursion deformation parameters and noise variance are adopted, and switching and unified correction are carried out when a reference point is unstable. According to the method, the unwrapping error under the non-stationary deformation is reduced, the scatterer density is maintained, the increment processing efficiency is remarkably improved, and near real-time monitoring is supported.