115 results about "Radar remote sensing" patented technology

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

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 discloses a hyperspectral image and laser radar data collaborative classification method and system based on a multi-modal mutual guidance attention network. The method comprises the following steps: obtaining a hyperspectral image and laser radar data; preprocessing the hyperspectral image and the laser radar data to obtain to-be-processed data, wherein the to-be-processed data comprises to-be-processed hyperspectral data and to-be-processed radar data; performing mask processing on the to-be-processed hyperspectral data based on the elevation information of the to-be-processed radar data to obtain hyperspectral modal features; performing mask processing on the to-be-processed radar data based on the spectral information of the to-be-processed hyperspectral data to obtain laser radar modal features; performing cross-modal interaction fusion on the hyperspectral modal features and the laser radar modal features to obtain a fusion result; and using a pre-trained classifier to classify the fusion result to obtain a classification result. The method can effectively improve the classification precision of the ground object pixel sample in the remote sensing image, and provides an efficient and robust solution for the high-precision collaborative classification of hyperspectral and laser radar remote sensing images.

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 discloses a flood monitoring and deduction method based on unmanned aerial vehicle time sequence radar remote sensing, which comprises the following steps: S1, collecting multi-source flood disaster high-spatial-resolution time sequence radar remote sensing data through unmanned aerial vehicle radar remote sensing, and processing the data into a remote sensing data set with consistent space-time reference; s2, preprocessing each time sequence radar remote sensing image in the remote sensing data set; s3, segmenting a submerged area and a non-submerged area of the preprocessed time sequence radar remote sensing image, removing fine water in the submerged area, and extracting a radar remote sensing high-resolution submerged range; and S4, quantitatively extracting flood evolution characteristic elements, and performing flood disaster potential influence area deduction on the current submerging range in combination with the digital elevation model and hydrological connectivity. According to the method, the problem that the large-range flood evolution process is difficult to dynamically monitor is solved, and support is provided for rapid emergency and risk avoiding transfer of watershed flood by forward deduction of the potential influence area of flood disasters.

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 provides a landslide area detection method and system based on radar remote sensing data. The method comprises the following steps: extracting a backscattering coefficient and a coherence coefficient when a detection radar detects a target scanning area under each time phase from the radar remote sensing data; determining the earth surface deformation sensitivity of each pixel point in the target scanning area based on a scattering characteristic matrix constructed by the backscattering coefficient under each time phase in combination with the spatial-temporal change characteristics of the coherence coefficient; according to the singular value decomposition result of the scattering characteristic matrix and the earth surface deformation sensitivity of each pixel point, dividing a deformation sensitive area and a stability subarea; according to the scattering entropy of the pixel points in the deformation sensitive area, hidden landslide points of the target scanning area are identified from the stability subarea, and then a landslide risk probability graph is generated through all the hidden landslide points in combination with the deformation sensitive area. According to the technical scheme provided by the invention, the hidden landslide point in the scanning area can be effectively identified when the weak deformation signal is submerged.

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 relates to a de-icing early warning method and device for a power transmission tower. The method comprises the following steps: constructing a first training data set, wherein the first training data set comprises meteorological data, radar remote sensing data, satellite remote sensing data, analysis data and meteorological derivative data; a weather prediction model is constructed, the weather prediction model comprises a long-short-term memory network and a Transform model, weather data prediction values are output through the trained weather prediction model, and the weather data prediction values after the current moment are judged to determine a current deicing mode; finite element models of different power transmission towers are constructed to construct a second training data set; training a structure response prediction model by using the second training data set to obtain a trained structure response prediction model; inputting the trained structure response prediction model to obtain a response parameter prediction value; and substituting the response parameter prediction value into various failure functions to calculate a failure value, and comparing the failure value with a corresponding early warning threshold to output an early warning result. By adopting the method, the early warning accuracy can be improved.

The invention relates to a rapid calculation method for a lake water surface roughness parameter, which comprises the following steps: acquiring a lake water surface height standard deviation and a surface slope initial value by using a sea wave spectrum model, and calculating an initial value of a related length parameter; and constructing a backscattering coefficient simulation model by taking the initial values of the height standard deviation and the related length, the lake temperature and the salinity as inputs, actually measuring a backscattering coefficient value by combining a radar remote sensing image, and solving the related length and the height standard deviation, namely a surface roughness parameter, by utilizing a least square method. By adopting the method disclosed by the invention, the lake water body surface roughness can be quickly calculated by utilizing radar remote sensing image data, and the algorithm has universality and stability in an inland lake; besides, large-scale and long-time-sequence lake water body surface roughness parameters can be obtained, data support is provided for lake water body physical property monitoring based on radar remote sensing image data, and the method has important significance on development of inland lake water body research based on microwave data.

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 present invention discloses a method for inverting understory biomass, relating to the field of remote sensing collaborative inversion technology. An observation formula for total forest surface biomass is determined based on radar remote sensing data, optical remote sensing data, and total forest surface biomass for a historical region. Radar remote sensing data and optical remote sensing data for a target region are substituted into the observation formula to determine the total forest surface biomass for the target region. The average leaf area index for the target region is inverted based on the optical remote sensing data for the target region. Leaf biomass is calculated based on the average leaf area index and the dry matter weight of leaves per unit area. The understory vegetation and litter biomass for the target region are determined based on the total forest surface biomass, leaf biomass, and aboveground woody biomass for the target region. This method can accurately calculate the understory biomass for the target region.

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 invention relates to a water area extraction method and device based on a radar remote sensing image, a computer program product and electronic equipment, and belongs to the field of remote sensing monitoring. The water area extraction method comprises the following steps: acquiring a radar remote sensing image, an optical remote sensing image and topographic data of a water area to be extracted; preprocessing the radar remote sensing image and the optical remote sensing image; carrying out adaptive filtering processing on the preprocessed radar remote sensing image; calculating a water body index image of the to-be-extracted water area, and obtaining a preliminary extraction result of the to-be-extracted water area according to the water body index image; and according to a classifier constructed by adding a gradient condition and a vegetation coverage condition, carrying out reclassification on the preliminary extraction result to obtain a final extraction result of the water area to be extracted. According to the scheme, the radar image water area extraction precision can be improved, the influence of mountain shadow and low shrub vegetation noise on the water area boundary is reduced, and the intelligent level of water area information monitoring is 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.