84 results about "Radar remote sensing" patented technology

The invention relates to the technical field of radar remote sensing image processing, in particular to a multi-scale synthetic aperture radar flood detection method and device, and the method comprises the steps: collecting a plurality of flood disaster SAR images of a flood region, and carrying out the preprocessing of the images, so as to obtain a standard flood disaster SAR image; dividing standard flood disaster SAR images, and constructing training, verification and test data sets; based on a multi-scale feature extraction network and a multi-head self-attention mechanism, constructing a multi-scale SAR flood detection network model, training the multi-scale SAR flood detection network model by using the training data set and the verification data set, and inputting the test data set into the trained multi-scale SAR flood detection network model, therefore, the influence of speckle noise is effectively suppressed, the capability of distinguishing flood from confusion-prone ground features in a complex scene is improved, and the accuracy and robustness of SAR image flood detection are improved.

The invention relates to the technical field of radar remote sensing, discloses a high-order loose body historical activity detection method based on multi-source DEM data, and aims to solve the problem that the historical activity of a high-order loose body in a glacier coverage area is difficult to accurately and quantitatively evaluate in the prior art. The scheme mainly comprises the steps that drainage basin units are automatically divided based on DEM data, and glacier influence areas are screened; multi-source DEM data are fused, and system errors are eliminated through geographic positioning deviation correction, elevation distortion deviation correction and track mode deviation correction; establishing a penetration depth physical model, and correcting the radar penetration depth in the glacier area by using the C / X wave band elevation difference optimization parameter; calculating an elevation change rate by adopting robust regression; and calculating an activity comprehensive index by integrating multiple factors, and identifying a hot spot region and a time period through probability estimation and spatio-temporal clustering. According to the method, automatic and high-precision quantitative evaluation on the historical activity of the high-position loose body is realized, and the method is particularly suitable for geological disaster early warning and risk evaluation in high mountain areas.

The invention provides a wake flow redirection control method and system based on cabin type laser radar prediction, and relates to the technical field of laser radar remote sensing detection, and the method comprises the steps: obtaining first wake flow propagation path data of a target unit at different spatial positions and first wake flow speed data of different time points through a cabin type laser radar; performing correlation analysis on the first wake flow propagation path data, the first wake flow speed data and a preset unit wake flow speed attenuation coefficient to obtain a correlation analysis result; obtaining a wake flow prediction result based on the first wake flow propagation path data and the correlation analysis result; obtaining wake flow redirection control demand data based on a preset wake flow control target and the calibrated first wake flow speed data; converting the pulse parameter into a pulse control signal based on wake flow redirection control demand data; based on the pulse control signal, accurate redirection control over the wake flow of the wind turbine generator is achieved, cross interference of the wake flow is reduced, and the overall power generation efficiency of a wind power plant is improved.

The invention discloses a rock mass RQD probability distribution dynamic reconstruction method based on digital twinning. Systematic technical breakthrough is achieved in the aspects of multi-source data fusion, three-dimensional heterogeneity modeling, space probability analysis, a dynamic updating mechanism and the like. By integrating drilling, radar, remote sensing, field sensing and other multi-source information and introducing a dynamic feedback mechanism, real-time sensing and dynamic modeling of rock mass structure continuity, heterogeneity and integrity changes can be achieved, and the spatial resolution and time response capacity of RQD evaluation are effectively improved. Compared with a traditional point location value measurement mode, the method achieves the conversion of a rock mass integrity index from discrete sampling to regional probability distribution, and remarkably enhances the comprehensive perception and fine control capability of a complex underground rock mass environment. According to the overall technical scheme, timeliness and accuracy of risk identification in rock mass engineering are improved, scientific decision support for support design and construction strategies is enhanced, and the method has good engineering adaptability and popularization and application value.

The invention provides a loose accumulation body stability analysis method and system based on a remote sensing image, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: delimiting a monitoring region through the terrain boundary of a loose accumulation body, continuously capturing multi-period remote sensing images through an optical and radar remote sensing equipment double-track system, and synchronously collecting environment action data; generating a feature response time sequence library recording corresponding change relations, extracting feature change rules from the feature response time sequence library to establish feature response anchor points, connecting the feature response anchor points in series to form a dynamic feature response chain, capturing real-time data in real time, comparing to generate deviation conduction topology, performing fluctuation tracing to position a starting anchor point, and analyzing environmental inducements. And related information is integrated to generate a stability analysis result containing a risk anchor point position, a deviation diffusion range and targeted prevention and control measures, and the stability analysis result is output to a monitoring terminal in a visual form, so that the geological disaster prevention and response capability is effectively improved.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The invention relates to the technical field of radar remote sensing monitoring, in particular to an airport runway seaworthiness intelligent evaluation method and system based on satellite-ground collaborative awareness, and the method comprises the steps: calculating the signal prominence intensity through the local saliency based on the SAR image pixel amplitude, and primarily screening highlight pixel points; a side lamp possibility index is constructed by combining double constraints of a straight line distribution rule and time sequence position reproducibility, and high-credibility side lamp pixel points are accurately identified from a low-signal-to-noise-ratio background; reversely positioning a runway pixel point area based on the matched parallel edge lamp fitting straight line; constructing a local deformation reference surface by taking an unwrapping phase of adjacent edge lamp pixel points as a benchmark, calculating the reliability of the runway and reconstructing a phase of an untrusted point, and generating a denoised image for airworthiness evaluation; according to the airport runway airworthiness evaluation method, the runway sidelight is used as a phase reference for time-space stability, the problem of phase incoherence caused by specular reflection in a flat runway area is effectively solved, and the accuracy and robustness of airport runway airworthiness evaluation are remarkably improved.

The invention relates to the technical field of radar remote sensing, and discloses a high-precision deformation monitoring and target imaging method based on MIMO-SAR. The method comprises the following steps: receiving an original MIMO-SAR echo data stream and a Beidou navigation system positioning data stream acquired by an image sensor; performing standardized preprocessing on the original echo data to generate standard radar data blocks; aligning radar data blocks and Beidou positioning data time and space, and establishing a mapping relation between data and a spatial position; performing sub-aperture division on the aligned data blocks to generate a multi-sub-aperture data set; performing beam forming processing on each sub-aperture data set to obtain a multi-view complex image; extracting an interference phase of a plurality of images and carrying out geometric correction by using Beidou data; unwrapping the corrected phase to obtain an absolute phase field; inverting a millimeter-level deformation sequence based on the absolute phase field; dynamically updating imaging focusing parameters according to the deformation sequence time evolution characteristics; and performing high-resolution imaging on subsequent data blocks by using the updated parameters, and outputting a deformation monitoring result and a target image.

The invention discloses a laser radar individual tree segmentation method based on multi-scale feature fusion, and relates to the technical field of laser radar remote sensing forestry resource investigation, and the method comprises the steps: point cloud data preprocessing and multi-scale adaptive sampling, combined denoising in combination with geometric distance and intensity information, and sampling parameter adjustment according to stand density; multi-scale feature extraction and cross-scale association are carried out, and local, morphological and global features and cross-scale association features are extracted; fusing and enhancing dynamic attention features, and constructing a feature pyramid; performing density peak clustering and initial segmentation, adaptively adjusting clustering parameters, screening core points and aggregating adjacent points; segmentation result optimization and morphological constraint are carried out, boundaries are optimized through curvature adjustment, ellipse fitting and closed operation, and abnormal units are corrected in combination with morphological verification. According to the method, the individual tree segmentation precision and form conformity are improved, and different stand densities and point cloud conditions are dynamically adapted; the method is suitable for diversified scenes from low-density pure forests to high-density mixed forests.

The invention provides a flood susceptible area prediction method based on multi-source remote sensing data, which constructs flood susceptible factors by fusing radar remote sensing, optical remote sensing, soil moisture, rainfall, river and topographic data, and evaluates and predicts flood susceptibility based on a machine learning model. The method comprises the following steps: S1, acquiring multi-source remote sensing data of a research area and preprocessing the multi-source remote sensing data; s2, calculating the flood probability of each pixel after the flood event based on the radar remote sensing image; s3, calculating related factors of terrain, hydrology, vegetation, soil moisture and rainfall, and constructing a flood susceptible factor set; s4, adopting XGBoost to train a flood susceptible model, and optimizing an input variable set; and S5, when a flood event is about to occur, inputting latest remote sensing data, calculating a flood easy-to-occur score, and evaluating a flood risk. Multi-source remote sensing data and a machine learning technology are utilized, the flood susceptibility prediction precision is improved, and the method can be widely applied to the field of flood disaster monitoring and early warning.

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 spaceborne SAR (Synthetic Aperture Radar) speed rapid estimation method, system and product based on fractional Fourier transform, belongs to the technical field of radar remote sensing, and solves the problems that the SAR imaging quality is low due to inaccurate spaceborne SAR speed estimation, and the SAR imaging quality is poor due to the fact that a method based on amplitude or phase is used for spaceborne SAR speed estimation in the prior art. The calculation complexity is high; the time consumption is long; and the estimation precision is limited. SAR echo data are obtained, and coarse estimation is carried out on the SAR speed; sAR imaging is carried out based on SAR echo data to obtain an SAR image, and coarse focusing is carried out on the SAR image in combination with the coarse estimated SAR speed; estimating the residual azimuth modulation frequency of the coarsely focused SAR image by adopting fractional Fourier transform; and obtaining accurate estimation of the SAR speed based on the roughly estimated SAR speed and the residual azimuth modulation frequency. The method is used for realizing rapid and accurate spaceborne SAR speed estimation.

The invention discloses a high-precision InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring method combining polarization information and deep learning, and particularly relates to the crossing field of radar remote sensing and deep learning technologies. The method comprises the following steps: acquiring multi-temporal complete polarization or dual polarization SAR original data, generating single-view complex data through radiometric calibration, registration and multi-view processing, and extracting a polarization coherence matrix; constructing a sample set containing polarization features and interferometric phase labels; designing a double-branch deep learning network model composed of a polarization feature extraction branch and a phase optimization branch, and processing a polarization matrix and a phase sequence by adopting a three-dimensional convolution structure and a long and short term memory-convolution hybrid structure; the deep polarization features and the optimized phase information are fused through the trained model, and the deformation quantity is output through regression calculation; and finally, generating a deformation graph under a geodetic coordinate system through geocoding. The polarization scattering characteristic is effectively utilized, and the monitoring precision and reliability in a complex scene are improved.

The invention discloses a geological disaster safety risk assessment method and system for a power grid region, which is applied to the field of power system safety and disaster risk assessment, and comprises the steps: obtaining remote sensing deformation monitoring data and power grid operation monitoring data of a target power grid region; performing time sequence analysis and deformation calculation on the remote sensing deformation monitoring data to generate deformation time sequence data; converting the deformation data into a deformation characteristic quantity associated with the power grid key facility; performing time sequence alignment and fusion processing on the multi-source data, and constructing a comprehensive operation risk fusion data set; and obtaining a risk critical value through a time sequence prediction model based on the fused data set, and performing risk quantitative evaluation and load adjustment according to the risk critical value. According to the invention, through fusion of radar remote sensing deformation monitoring and power grid operation data and combination of the deep learning prediction model, accurate assessment and advanced early warning of geological disaster risks of key facilities of the power grid are realized, and the active defense capability and operation toughness of the power grid to geological disasters are effectively improved.

This invention discloses a smart metasurface, system, and method for measuring two-dimensional direction of arrival (DOA). The metasurface consists of a metal patch layer, an F4B dielectric layer, and a metal backplane layer, comprising n*n basic units. Each unit can be independently controlled by a periodic control voltage. By designing the control voltage signal for each unit, the reflection phase of each unit exhibits a periodic and mutually orthogonal phase change in space. The signal is then received by an auxiliary antenna and processed to obtain the two-dimensional DOA on the smart metasurface. This invention achieves accurate two-dimensional DOA estimation using only a periodic control voltage applied to the metasurface and a single receiving antenna, compared to methods requiring n*n base plates. 2 Compared to a phased array system of the same scale for two-dimensional direction-of-arrival estimation with a single radio frequency front-end, its architecture is simple, significantly reducing hardware costs and facilitating integration. It can be used in multiple fields such as radar, remote sensing, and wireless communication, and has broad application prospects.

The invention relates to the technical field of agricultural remote sensing, in particular to a harvesting monitoring method and device based on multi-source remote sensing data. If the NDVI data is the optical remote sensing data, calculating a cloud coverage ratio by using the cloud wave band data, and if the cloud coverage ratio is low, comparing each pixel value of the NDVI data with an optical harvesting judgment threshold to obtain a latest harvesting ratio; if the rainfall data is radar remote sensing data, calculating daily average rainfall by using the rainfall data; if the average daily precipitation is low, comparing each pixel value of the vh polarization data with a radar harvesting judgment threshold value to obtain a harvesting proportion; updating the effective harvesting proportion according to the harvesting proportion. According to the technical scheme, the optical remote sensing data and the radar remote sensing data are combined to jointly judge the effective harvesting proportion of the land parcel, meanwhile, the influence of weather on the remote sensing data is overcome to a certain extent, the requirements for timeliness and accuracy are met, and the probability of harvesting misjudgment caused by data noise is reduced.

The invention relates to the technical field of radar remote sensing measurement, in particular to a method and a system for measuring snow thickness by using a dual-carrier-frequency interference radar. The method comprises the following steps: generating a linear frequency modulation signal, respectively modulating the linear frequency modulation signal to a first carrier frequency channel and a second carrier frequency channel, and synchronously transmitting the linear frequency modulation signal to a snow area; synchronously receiving echo signals of the two channels, and respectively carrying out carrier frequency removal and dechirp processing on the echo signals of the two channels to obtain baseband echo signals of the two channels; respectively converting the baseband echo signals of the two channels into a frequency domain, executing high-order phase removal processing, and then converting the baseband echo signals back to a time domain to obtain high-order phase removal time domain echo signals of the two channels; performing interference operation on the high-order phase removed time domain echo signals of the two channels to obtain an interference baseband signal between the two channels; and carrying out distance compression on the interference baseband signal to obtain an echo frequency spectrum and then carrying out parameter estimation so as to output a measurement result of the snow thickness corresponding to the distance difference between the snow surface and the underlying interface.

The invention discloses a crop height inversion method and device based on compact polarimetric SAR data, and relates to the technical field of polarimetric radar remote sensing quantitative inversion, and the crop height inversion method based on compact polarimetric SAR data mainly comprises the steps: carrying out the data preprocessing of an original polarimetric SAR image, and obtaining compact polarimetric data; a multi-dimensional feature set is obtained according to polarization parameters, a machine learning algorithm model is trained and verified by combining a field actually-measured crop height data set to obtain a height prediction model, and crop height estimation data is obtained accordingly; and feature subset optimization is carried out by using a forward feature selection method, the machine learning algorithm model is trained and verified again based on the optimized feature subset to obtain a height prediction model, and the optimized feature subset is predicted to obtain a higher-precision crop height inversion result. According to the crop height inversion method and device based on the compact polarimetric SAR data, the inversion efficiency and precision can be effectively improved.

The application discloses a power transmission line icing thickness deduction and early warning method and system based on SAR images, relates to the technical field of power transmission line icing monitoring, and aims at the problem that existing radar remote sensing icing monitoring can only qualitatively distinguish, cannot quantitatively and accurately calculate the icing thickness, and is difficult to predict the icing evolution trend. A background scattering reference vector is constructed by extracting the characteristics of a non-icing full-polarization SAR reference image. The characteristics of an icing monitoring image are extracted and compared with the background vector to determine the net icing characteristic vector. The standard icing characteristic vector is obtained based on the observation geometry parameter correction, and the icing dielectric parameter is determined. The forward mapping of the icing thickness and the theoretical echo response is established. The current icing thickness is obtained by substituting the measured echo response into the forward mapping and inversely optimizing. The evolution data are generated by driving the forward mapping with the current thickness as the initial state for time-space deduction. The vulnerability is determined according to the evolution data, and the grading early warning strategy is configured. The quantitative calculation, dynamic deduction and accurate early warning of the icing thickness under complex weather are realized.

The application provides a building layering intelligent recognition method and system based on multi-modal remote sensing data, and belongs to the technical field of building detection. Multi-modal and multi-view remote sensing images including optical remote sensing images and radar remote sensing images are continuously acquired; the remote sensing images are input into a deep learning prediction model to realize accurate prediction of the height of urban building groups; the building height information predicted at multiple time nodes is calculated by a difference analysis method and a change vector analysis to obtain the change value of height data in different periods; the buildings whose height change values exceed a first threshold value are preliminarily marked as layering buildings; a weighted local spatial anomaly measurement method is further applied to calculate a spatial consistency index of height change in the region, and if the index exceeds a second threshold value, the building is finally determined as a layering building. The application can realize automatic and intelligent identification of illegal layering buildings in urban areas, and provide a scientific basis for urban management and law enforcement.

This invention discloses a method, system, terminal device, and storage medium for inverting crop lodging parameters. The method includes: acquiring satellite optical radar remote sensing information and UAV optical radar remote sensing information; extracting features from the satellite optical radar remote sensing information and UAV optical radar remote sensing information to obtain remote sensing image feature information; fusing multi-source heterogeneous information from the remote sensing image feature information to construct a remote sensing image dataset; inputting the remote sensing image dataset into a deep neural network model for model training to obtain a crop lodging parameter inversion model; acquiring crop lodging image data; and inputting the crop lodging image data into the crop lodging parameter inversion model to obtain crop lodging parameters. This invention obtains rich crop lodging information by fusing satellite optical radar remote sensing information and UAV optical radar remote sensing information, and then inverts the lodging parameters through a parameter inversion model to obtain high-precision crop lodging parameters.

The invention relates to the technical field of corner reflector adaptive adjustment, in particular to a corner reflector adaptive adjustment method, system and device based on multi-source radar remote sensing data. Comprising the following steps: S1, acquiring an installation position of a corner reflector in a terrain lifting complex scene of a power transmission line, extracting terrain parameters according to the installation position, and extracting an interference type according to the terrain parameters; the method accurately adapts to the specific requirements of the terrain uplift complex scene of the power transmission line, achieves the accurate recognition of the terrain uplift complex scene, the comprehensive extraction of terrain parameters and the directional judgment of interference types through the access of power transmission line management end data and a digital elevation model, combines the complementary advantages of multi-source radar data, and achieves the accurate recognition of the terrain uplift complex scene. Quantitative inversion and prediction of interference parameters are completed, the problems of fuzzy interference identification and insufficient data support in complex terrains in the prior art are effectively solved, and the stability of radar echo signals and the accuracy of pole and tower state monitoring data are remarkably improved.

The invention discloses a jungle penetration positioning and multi-mode converged communication guarantee method and system based on an unmanned aerial vehicle load, and relates to the technical field of emergency communication and radar remote sensing. An air comprehensive load platform is deployed over a rescue area, and an emergency communication base station module and a multi-mode converged communication module carried by the air comprehensive load platform are activated; the emergency communication base station module provides public network and / or private network communication coverage for the ground, and the multimode converged communication module establishes a data transmission link with a ground command center through satellite communication and / or relay communication; the air comprehensive load platform carries a synthetic aperture radar (SAR) module with a jungle penetrating power frequency band, and a transmitter of the SAR module executes frequency stepping signal transmission and transmits an inquiry signal to a ground jungle area. According to the invention, through the air comprehensive load platform, the P-band SAR radar, the active beacon response and the multimode communication link, rapid deployment of a communication network, positioning of target personnel and real-time situation return in a complex jungle environment are realized.

This invention discloses a landslide monitoring method based on remote sensing data, comprising: acquiring high-resolution optical remote sensing images and synthetic aperture radar images of the study area; performing registration processing on the optical and radar remote sensing images to obtain remote sensing images with accurate spatial location and geometric shape; generating a high-spectral-resolution fused image of the landslide area; obtaining matched high-resolution DEM data; calculating slope parameters of the study area based on the matched high-resolution DEM data, and determining a slope threshold according to slope distribution characteristics and prior knowledge; extracting slope aspect parameters of the study area using a grid-and-vector-based slope aspect algorithm, and filtering out landslide-prone areas in conjunction with the slope threshold; obtaining a landslide hazard distribution map; obtaining the probability and hazard level of landslide occurrence, and obtaining the landslide situation based on the probability and hazard level of landslide occurrence. This invention achieves accurate identification and dynamic monitoring of landslide disasters.

The present application relates to the technical field of radar remote sensing monitoring, in particular to an airport runway airworthiness intelligent evaluation method and system based on star-ground cooperative perception, which highlights the signal strength by calculating the local saliency of SAR image pixel amplitude, and preliminarily screens the high-light pixel points; the runway possibility index is constructed by combining the double constraints of straight line distribution law and time sequence position reproducibility, and the high-credibility edge light pixel points are accurately identified from the low signal-to-noise ratio background; the runway pixel point area is reversely positioned based on the matched parallel edge light fitting straight line; the local deformation reference surface is constructed based on the unwinding phase of adjacent edge light pixel points, the runway credibility is calculated, the phase of the untrusted points is reconstructed, the denoised image is generated, and the airworthiness evaluation is performed; the present application uses the runway edge light as the phase reference benchmark with space-time stability, effectively solves the phase incoherence problem caused by mirror reflection in the flat runway area, and significantly improves the accuracy and robustness of the airport runway airworthiness evaluation.

This invention relates to a method for improving the accuracy of seawater profile data obtained by lidar remote sensing. It employs a laser transmitter and an optical receiver to emit laser light and receive Raman and elastic scattered light. A processor calculates the ratio of the elastic scattered main wave signal to the Raman scattered signal at the elastic main frequency, thus largely eliminating the influence of path extinction in the observed values. By measuring seawater Raman scattering, this invention eliminates the effects of scattering and absorption along the light path, increasing the accuracy of lidar measurements of the ocean's vertical profile and improving our understanding of the marine environment.

The invention discloses a multi-band polarized SAR (Synthetic Aperture Radar) image ground object classification method based on wavelet enhancement Swin Transform, and belongs to the technical field of radar remote sensing image processing. Comprising the steps of polarized SAR image preprocessing, image block division, Swin Transform branch feature extraction, wavelet transform branch feature extraction, feature dimension alignment, cross-band attention feature fusion, classification prediction, loss function optimization and end-to-end training optimization. Through a cross-band attention fusion module, a multi-directional cross attention mechanism and learnable weighted residual connection are adopted to realize adaptive deep fusion of spatial domain and multi-band polarization features, and the method solves the contradiction between global context modeling and local detail keeping in a traditional method. The precision and robustness of multi-band polarized SAR image ground feature classification are significantly improved, and a new technical approach is provided for accurate ground feature recognition in a complex ground surface environment.

The application provides a loose accumulation body stability analysis method and system based on remote sensing images, and relates to the technical field of geological disaster monitoring. First, the monitoring area is delimited with the topographic boundary of the loose accumulation body. The multi-time remote sensing images are continuously captured by the optical and radar remote sensing equipment double-track system, and the environmental action data are synchronously collected. The feature response time sequence library recording the corresponding change relationship is generated. The feature change rule is extracted from the feature response time sequence library to establish the feature response anchor point. The dynamic feature response chain is formed in series. The real-time data are captured and compared to generate the deviation conduction topology. The fluctuation source positioning starting anchor point is located, and the environmental inducement is analyzed. The stability analysis results including the risk anchor point position, the deviation diffusion range and the targeted prevention and control measures are integrated to generate the stability analysis results, which are output to the monitoring terminal in a visual form. The ability of geological disaster prevention and response is effectively improved.

The present application relates to the field of agricultural remote sensing technology, in particular to a kind of based on multi-source remote sensing data's harvesting monitoring method and device, can obtain the latest remote sensing data of the monitored plot;If optical remote sensing data, calculate cloud coverage ratio using cloud band data, if cloud coverage ratio is low, each pixel value of NDVI data is compared with optical harvest judgment threshold respectively, obtain the latest harvesting ratio;If radar remote sensing data, calculate daily average rainfall using precipitation data;If daily average rainfall is low, each pixel value of vh polarization data is compared with radar harvest judgment threshold respectively, obtain harvesting ratio;According to harvesting ratio, update effective harvest ratio.The technical scheme combines optical remote sensing data and radar remote sensing data, jointly judges the effective harvest ratio of plot, at the same time, to a certain extent, overcome the influence of weather on remote sensing data, both meet the timeliness and accuracy requirements, and reduce the harvesting misjudgment probability caused by data noise.

The invention relates to the technical field of crossing of radar remote sensing, electromagnetic scattering simulation and computer vision, in particular to an electromagnetic scattering large model and complex earth surface scattering characteristic learning method, and aims to solve the problems that textures in large-scene SAR simulation are difficult to vivid and are inconsistent with actually measured scattering. In the first stage, based on a differentiable electromagnetic scattering model, inversion is carried out from multi-source remote sensing data to obtain a high-resolution scene parameter diagram, ground feature scattering characteristics are represented, and multi-angle and high-fidelity scattering coefficient simulation is realized; and in the second stage, a deep learning network is constructed, cross-modal mapping from data such as optical images to a scene parameter graph is realized under the conditions of physical constraint and reversible normalization, and the method is used for extended simulation of an area lacking actually measured SAR data. The method has both physical interpretability and efficient generalization ability, can generate multi-angle scattering data with real textures and consistent scattering, and is suitable for large-scene SAR simulation, system calibration and intelligent interpretation.