99 results about "Radiometric calibration" patented technology

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 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 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.

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.

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.

The application discloses a kind of multi-temporal absolute radiometric calibration method of remote sensing satellite, using multi-star absolute radiometric calibration coefficient, obtain the apparent reflectivity of multi-star target point;Using the linear relationship of the apparent reflectivity of multi-star target point and the real reflectivity of multi-star target point;Determine the linear slope deviation value under multiple times by multiple linear relationship.The multi-temporal absolute radiometric calibration method of remote sensing satellite disclosed by the application monitors the absolute radiometric calibration accuracy of different calibration periods of multi-star to evaluate the stability of remote sensor responsivity.

The invention relates to the technical field of ocean remote sensing, and discloses a task processing method, device and equipment based on ocean polarization direction consistency characteristics and a medium, which can respond to a processing instruction for an ocean observation task and perform radiometric calibration, correction, neighborhood window selection and characteristic parameter setting on polarization remote sensing original observation data of a target sea area. And obtaining effective pixel data, selected neighborhood window parameters and characteristic parameters for adapting to the extraction requirements of the ocean polarization direction consistency characteristic PDCF (Packet Data Convergence Function). And according to the neighborhood window parameters, the feature parameters and the created ocean PDCF index definition formula, performing PDCF extraction in the effective pixel data to obtain a PDCF map. And generating a corresponding multi-polarization feature fusion image according to the PDCF map. And inputting the multi-polarization feature fusion image into the trained marine observation task processing model for task processing to obtain a corresponding task processing result, so that the processing speed of the marine observation task is effectively improved, and the real-time performance of marine observation task processing is enhanced.

A high-precision radiometric calibration method for very high sensitivity infrared sea imaging data is proposed. Firstly, the blackbody calibration data on the infrared remote sensor satellite are used to calculate the radiometric calibration coefficient and detect the blind pixels. Then, the image data are corrected by the primary relative radiometric correction and blind pixel compensation correction. Secondly, a radiometric correction method based on small sample statistics is proposed to remove the residual stripe noise after the primary correction, which further improves the radiometric quality of the infrared image. Finally, the blind pixels in the image are detected and removed.

This invention discloses a geological disaster early warning method based on remote sensing technology, belonging to the field of geological disaster early warning technology. The method includes: acquiring remote sensing image data with at least two different imaging principles within the same geographic unit and the same observation time window; performing geometric correction on the remote sensing image data under a unified coordinate system; and converting the remote sensing image data into corresponding physical quantity data and completing scale consistency processing through sensor response conversion and radiometric calibration; using the remote sensing image data and digital elevation data under the unified coordinate system, calculating the slope and aspect of each pixel, and constructing the light propagation path based on the solar altitude angle and solar azimuth angle. This invention models the influence range of environmental factors from the perspective of propagation structure by constructing light propagation paths and heat diffusion propagation paths under a unified coordinate system and calculating the direction vector field formed by the gravity direction of the slope. This allows anomaly identification to be based not only on numerical changes but also on the analysis of propagation mechanisms.

This invention provides a deep learning-based adaptive calibration method and apparatus for SAR images. The method involves acquiring SAR image data and preprocessing it to obtain processed data; inputting the processed data into a deep learning model, which then outputs scene classification results; matching the scene classification results with a corresponding calibration strategy and calculating calibration constants; and using these calibration constants to perform radiometric calibration on the SAR image data. This method can automatically identify SAR image scene types and autonomously select the optimal calibration strategy and parameters based on scene characteristics, thereby significantly improving calibration accuracy and efficiency, reducing manual intervention, and increasing the level of operational automation.

This invention provides a method, device, terminal, and storage medium for soil Munsell color discrimination based on high-resolution multispectral remote sensing. The invention acquires multispectral remote sensing images and metadata, performs radiometric calibration and atmospheric correction on the images to obtain surface reflectance; generates a quality mask including clouds, cloud shadows, shading, water bodies, and anomalous pixels, and removes unusable pixels; estimates soil purity by combining a bare soil candidate mask and gates mixed pixels; performs geometric and BRDF normalization on bare soil candidate pixels, and constructs soil state indicators from SWIR bands; establishes a mapping from reflectance to CIE tristimulus values ​​based on the sensor spectral response and converts it to CIELAB; obtains the equivalent color of dry soil by segmental compensation according to the soil state indicators; performs color difference matching with the Munsell color library and calculates the confidence level; outputs an undecidable condition and cause code when the confidence level is low, achieving stable discrimination and reliable output under complex observation conditions.

This invention discloses an underwater target reflectance inversion method based on an underwater transmission model, comprising: acquiring the original digital values ​​of the underwater target collected by a hyperspectral camera; combining the built-in radiometric calibration parameters of the hyperspectral camera; converting the original digital values ​​into radiance based on the sensor pre-calibration response characteristics; physically modeling the water absorption effect, scattering effect, and path radiation effect during underwater light propagation to determine the linear mapping relationship between radiance and target reflectance; constructing a training radiative transmission model using a non-negative linear regression model; using a calibrated target with known reflectance as a sample to train the radiative transmission model between the original digital values ​​and the target reflectance, and solving for the mapping parameters between the two; and based on the original digital values ​​and mapping parameters of the underwater target to be reconstructed, using the trained radiative transmission model to invert and obtain the true reflectance of the target, thus completing the spectral reconstruction.

This invention relates to the fields of remote sensing image processing and agricultural information technology, and in particular to a method for refined extraction of winter wheat planting areas based on Gaofen-2 imagery and edge-enhanced DeepLabV3+. The method includes: acquiring Gaofen-2 imagery and performing preprocessing such as radiometric calibration, atmospheric correction, geometric fine correction, and image fusion to construct a labeled training dataset; constructing an edge-enhanced DeepLabV3+ network, which adds an edge enhancement module to the standard DeepLabV3+ network to explicitly learn the edge features of winter wheat fields and inject edge information into the decoder; designing a joint loss function, including segmentation loss, edge loss, and boundary-aware loss, and training the network end-to-end; and using the trained network to perform sliding window prediction and post-processing on the image to be extracted to obtain the refined extraction results of winter wheat planting areas. This invention significantly improves the segmentation accuracy of winter wheat field boundaries through the edge enhancement module and boundary-aware loss.

The present disclosure provides a satellite in-orbit radiation calibration method, device and electronic equipment, comprising: obtaining a set of atmospheric parameters associated with a calibration field; using the set of atmospheric parameters and a first image data set of a first satellite sensor in the calibration field area, constructing a bidirectional reflectance distribution function model of the calibration field; according to the center wavelength of each channel in the first satellite sensor, linearly interpolating the coefficients of the bidirectional reflectance distribution function model to obtain a target model with target coefficients, wherein the target model is a spectrally continuous bidirectional reflectance distribution function model; using a second image data set of a second satellite sensor in the calibration field area, the set of atmospheric parameters and the target model, determining the correction relationship between the simulated apparent radiance simulated by the target model and the actual apparent radiance; using a third image data set of a satellite sensor to be calibrated in the calibration field area, the set of atmospheric parameters, the target model and the correction relationship to calculate the radiation calibration coefficient of the satellite sensor to be calibrated.

This invention discloses a space-ground coordinated radiometric characteristic calibration method for spaceborne active microwave sensors in the field of radar Earth observation technology. It constructs a space-ground coordinated calibration system consisting of a radiation reference source satellite and a ground calibration processing station. The method includes: determining the orbital parameters of the radiation reference source satellite; the satellite transmitting signals to the ground station in Earth observation mode, using a known RCS target on the ground to calibrate its own antenna gain and link gain; receiving signals from the satellite to be calibrated in Earth observation mode, accurately calculating and transmitting echo signals based on the calibrated parameters; and the satellite to be calibrated calibrating its own transmit / receive link gain based on the received echo signals. This invention can be widely applied to the on-orbit radiometric calibration of active microwave sensors such as synthetic aperture radar, altimeters, and microwave scatterometers, significantly improving calibration frequency and accuracy, supporting multi-band, high-stability quantitative remote sensing tasks, and providing reliable technical support for precise observation in fields such as ocean monitoring, weather forecasting, and topographic mapping.

The invention discloses an SAR marine oil spill intelligent identification method and system based on a diffusion model, and the method converts oil spill detection into a condition generation task, and comprises the steps: carrying out the geometric correction, radiometric calibration, noise suppression and normalization of an SAR image; a feature confidence modeling module CEM is adopted, and feature reliability is enhanced through space and channel double confidence calculation; constructing a conditional diffusion model, taking the SAR image as conditional guidance, and utilizing a noise estimation network of a U-Net architecture to carry out iterative denoising to generate a segmentation mask; five times of independent generation are executed, and the stability is improved through pixel-level average fusion and uncertainty quantization; extracting oil spill area parameters through morphological processing, and outputting the oil spill area parameters in a standard geographic information format. According to the system, the conditional denoising diffusion probability model is applied to the field of ocean remote sensing for the first time, pixel-level uncertainty estimation is achieved, the method is remarkably superior to an existing method, and a high-precision and high-reliability intelligent recognition scheme is provided for oil spill emergency response.

The invention discloses a remote sensing image relative radiation calibration method and system based on quantiles, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting satellite historical image data, and separating the data according to a spectral band; performing incremental sorting on pixel values of each column of the single-band image detector, and uniformly extracting quantile values to generate a quantile feature sequence of each column; calculating an arithmetic mean value of all columns under the same quantile level, and constructing global reference quantile distribution; constructing a linear mapping relation between each column quantile sequence and reference distribution based on a least square method, and obtaining radiation correction gain and bias coefficient of each detector column; correcting the original image column by column by using the coefficient; according to the method, vertical strip noise caused by non-consistency of sensor response is effectively eliminated, the limitation that traditional calibration is limited by meteorological conditions and platform maneuvering is overcome, and high-frequency and high-precision calibration timeliness and image radiation quality are remarkably improved.

The invention discloses a boiler furnace slagging degree online detection method based on multispectral vision, and particularly relates to the technical field of boiler operation monitoring, and the method comprises the steps: firstly, completing the installation and geometric and radiation calibration of a visible light camera and a thermal infrared imager under the boiler shutdown or low-load working condition, and building a unified space-time reference and physical quantity mapping relation; when a boiler runs, an industrial control computer synchronously collects and preprocesses multi-spectral images, after registration and fusion, a pre-training segmentation model is utilized to obtain a slag-bonding area mask, then the area, coverage rate, equivalent thickness and texture and temperature features of slag-bonding are extracted, and a slag-bonding feature vector is formed. And based on the vector, calculating a comprehensive scorification score and grading, and meanwhile, dynamically adjusting a sampling period in combination with a boiler working condition, a scorification risk and system resources. And when the score and the development trend exceed threshold values, an alarm is triggered, and equipment is controlled in a linkage manner, so that quantitative online monitoring and intelligent treatment of the slagging state of the heating surface of the boiler are realized.

The invention relates to a satellite-borne GNSS-S-based high-precision intelligent inversion method for soil humidity of a complex earth surface, which comprises the following steps of: acquiring multi-angle double-station image signals of the complex earth surface through a GNSS-S radar carried by a low-orbit satellite by taking a plurality of navigation satellites as signal sources; the data after multi-view processing is used for: (1) realizing full-link radiometric calibration from signal emission to signal reception by using a precision measurement device; (2) constructing a multi-parameter characteristic database containing surface physical characteristics and scattering characteristics; and (3) based on GNSS-S radar observation data and the feature database, establishing a deep learning intelligent inversion model to realize soil humidity quantitative inversion.

The invention discloses a methane smoke plume recognition and emission estimation method based on multi-constraint background selection, and belongs to the technical field of greenhouse gas remote sensing monitoring and hyperspectral inversion. According to the method, satellite-borne or airborne short-wave infrared hyperspectral observation data is subjected to preprocessing such as radiometric calibration, atmospheric correction and noise suppression, a multi-constraint background pixel screening model including same-column consistency constraint, spectrum slope consistency constraint and diffusion distance constraint is constructed, and a background spectrum library is automatically obtained; based on the background library, an enhanced matched filtering algorithm is adopted to extract the methane concentration enhancement amount, and the system deviation and the false alarm rate are remarkably reduced. A smoke plume area is accurately extracted through spatial filtering, morphological segmentation and connected domain analysis, and the methane emission flux is inverted by combining an integrated quality enhancement (IME) emission estimation model with meteorological wind field data.

This application provides a method and apparatus for radiometric calibration of thermal infrared satellites based on multi-energy-level targets, comprising: acquiring payload performance parameters of a thermal infrared satellite; acquiring transit information of the thermal infrared satellite; identifying multiple targets with different energy levels within the dynamic range of the thermal infrared satellite's payload based on the payload performance parameters and transit information; measuring the ground radiation parameters and atmospheric parameters of each target during the transit information; calculating the equivalent entrance pupil radiance of the target under the thermal infrared satellite based on the ground radiation parameters and atmospheric parameters; calculating the digital quantization value of the area where the target is located; and determining the absolute radiometric calibration coefficient of the thermal infrared satellite based on the digital quantization value and equivalent entrance pupil radiance of each target. This reduces the deviation in absolute radiometric calibration within the dynamic range of the thermal infrared satellite's payload, improving the accuracy of subsequent quantitative applications and comprehensive analysis.

This invention relates to the field of SAR image processing technology, specifically disclosing a SAR image topographic radiometric correction method that takes into account overlapping and shadowed areas. The method includes acquiring SAR images, DEM images, and source files within the study area; performing multi-view processing on the original SAR images, resampling the DEM images, and performing radiometric calibration; using an indirect positioning method to perform geometric positioning based on an RD model, generating lookup tables A and B, and registering the images; detecting pixels in overlapping and shadowed areas; calculating the effective scattering area corresponding to each SAR pixel; performing radiometric correction based on the effective scattering area, repairing the backscattering coefficient values ​​in shadowed areas, and finally performing geometric correction. This invention, to a certain extent, eliminates the topographic effects caused by the SAR imaging mechanism and terrain undulations, improving the radiometric correction effect in areas with large topographic undulations. The corrected product can be widely used in fields such as ground feature inversion and classification, deformation detection, and target detection.