134 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 discloses a high-precision topographic mapping system and method based on multi-source remote sensing data, and relates to the technical field of topographic mapping, and the method comprises the steps: obtaining an optical remote sensing image, a radar remote sensing image, LiDAR point cloud data and meteorological index data of a target region, and carrying out the data preprocessing of the optical remote sensing image, the radar remote sensing image and the LiDAR point cloud data; obtaining the surface feature types of the target area and the coverage area of each surface feature type, and carrying out the weighted fusion of the optical remote sensing image and the radar remote sensing image of the target area according to the surface feature types of the target area and the coverage area of each surface feature type, and obtaining a self-adaptive fusion remote sensing image; according to the method, points in LiDAR point cloud data are divided into distinguished ground points and non-ground points, a digital elevation model is constructed according to the ground points in the LiDAR point cloud data, three-dimensional modeling of the terrain is performed on the basis of the digital elevation model in combination with texture information of a self-adaptive fusion remote sensing image, a three-dimensional terrain model is generated, and the terrain surveying and mapping precision is remarkably improved.

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 relates to the technical field of radar remote sensing image processing, in particular to a single-polarization synthetic aperture radar change detection method and device.The method comprises the steps that initial single-polarization SAR images of front and back time phases are preprocessed to obtain a final image, a multi-scale feature difference chart is calculated, pseudo tags are generated through fuzzy C-means clustering, and the final image is obtained; a UNet coding and decoding structure is used as a basic framework, a double-branch convolutional neural network structure is designed, an input layer, a convolutional layer, a pooling layer, a full connection layer and an output layer are adopted to form a twin convolutional neural network, residual connection is established between an input tensor and an output tensor after network processing, a change detection model is generated, and a change detection result is obtained. And outputting a detection result of the single-polarization synthetic aperture radar by using the change detection model. Therefore, the problems of low accuracy of a change monitoring result and the like caused by the defects that the processing capacity of speckle noise in an SAR image monitoring method is limited and a static threshold segmentation method is difficult to adapt to the global situation in the prior art are solved.

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.

A non-leaf green organ coupled crop photosynthetic area index radar inversion method comprises the steps that S1, a photosynthetic area index CAI is calculated, and the photosynthetic area index CAI is equal to the sum of a leaf area index LAI and a non-leaf green organ area index NFAI; s2, crop microwave scattering characteristics are analyzed, and polarization decomposition is carried out on radar remote sensing data; s3, screening out a polarization characteristic combination with the strongest correlation with a photosynthetic area index (CAI); and S4, on the basis of the selected feature combination, utilizing a machine learning algorithm to construct an inversion model of the photosynthetic area index CAI and the preferable polarization feature, and utilizing the inversion model to perform inversion on the photosynthetic area index of the regional crop to obtain a regional inversion result. The invention further provides a non-leaf green organ coupled crop photosynthetic area index radar inversion system. According to the method, the photosynthetic area index CAI is provided, the leaf area and the non-leaf green organ area are comprehensively considered, the mechanism process of crop growth change and yield formation is scientifically reflected, and the crop growth and yield can be accurately monitored.

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 discloses a traction type landslide area identification method and system based on an InSAR technology, belongs to the technical field of geological disaster dynamic identification and monitoring, and effectively overcomes the limitation of a traditional method by obtaining surface deformation information through radar remote sensing data. The InSAR technology can cover a larger area, provides planar deformation characteristics, and reflects the overall deformation rule of the slope more completely. Meanwhile, the InSAR technology has the advantages of being rapid and efficient, a large amount of data can be obtained in a short time, and the traction type landslide recognition efficiency is remarkably improved. Compared with field geological survey and geophysical exploration, the InSAR technology is not limited by terrains, deformation information of areas difficult to reach can be obtained, data are objective and reliable, and influences of human factors are reduced. By comprehensively analyzing the multi-temporal and multi-track InSAR data, the boundary, form and deformation characteristics of the traction type landslide can be identified more accurately, and more accurate data support is provided for risk assessment and prevention of the traction type landslide.

The invention provides an SAR (Synthetic Aperture Radar) water body extraction method based on an scSE-U-Net model, and relates to the field of radar remote sensing and ecological environment remote sensing, and the method comprises the following steps: obtaining dual-polarization SAR data and Yellow River source SAR data, and making and synthesizing a false color data set in combination with a principal component analysis method; based on the U-Net model and an scSE attention mechanism, an scSE-U-Net model is constructed; training an scSE-U-Net model by synthesizing a false color data set; performing semantic segmentation on the SAR data of the Yellow River source through an scSE-U-Net model to obtain a water body range of the Yellow River source region; and obtaining a long-time sequence lake extraction monitoring result in the water body range of the source region of the Yellow River by using DEM data and an artificial visual inspection method, and completing the extraction of the lake water body of the source region of the Yellow River. Compared with a traditional monitoring extraction method, the technical scheme of the application can obtain a lake water body extraction result with higher precision.

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.

Method for classification of building damage induced by a natural disaster event; the method comprising: - obtaining microwave radar remote sensing data of a geographical region impacted by the natural disaster event, the remote sensing data having been generated after the natural disaster event; - obtaining building data from at least one geographical database, wherein the building data pertains to areas of buildings at outer edges of outside walls of said buildings; - enriching the remote sensing data with the building data; - segmenting the enriched remote sensing data into segments corresponding with buildings based on the building data; - extracting textural features from the segmented remote sensing data; - obtaining a machine learning, ML, classifier which has been trained to classify building damage of each segment of the segmented remote sensing data into a building damage level classification comprising at least two building damage levels; wherein the training is based on remote sensing data of a plurality of different types of natural disaster events and of at least two different types of geographical regions; and - classifying building damage of each segment of the segmented remote sensing data into the building damage level classification, using the ML classifier.

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.

The invention discloses an InSAR-based mining area settlement funnel identification method, a medium and equipment, and relates to the technical field of radar remote sensing and image processing, the InSAR-based mining area settlement funnel identification method mainly comprises the following steps: preprocessing a synthetic aperture radar image of a research area to obtain a real interferogram; marking to obtain a real interferogram data set; constructing a simulation interferogram, and combining with the real interferogram data set to obtain a mixed data set; training the deep learning model by using the mixed data set to obtain a target detection model; and predicting the to-be-identified aperture radar image of the research area by using the target detection model to obtain a settlement funnel distribution diagram. By implementing the InSAR-based mining area settlement funnel identification method, the medium and the equipment provided by the invention, the settlement funnel can be quickly, accurately and automatically detected on a large-range interferogram.

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.