19 results about "Radar remote sensing" patented technology

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.

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.

This invention discloses a method and device for geological disaster prevention and control of power transmission and transformation tower foundations, belonging to the field of landslide detection technology. The method involves: real-time acquisition of optical remote sensing images, radar remote sensing images, microwave remote sensing images, and thermal infrared remote sensing images; overlaying optical and radar remote sensing images to generate slope topographic spatial distribution data; analyzing vegetation growth status in the optical remote sensing images to obtain vegetation spatial distribution data; overlaying the slope topographic spatial distribution data and vegetation spatial distribution data to obtain topographic-vegetation coupled spatial distribution data, thereby determining the landslide sensitivity level of each preset grid unit and generating landslide sensitivity level spatial distribution data; and retrieving water storage spatial distribution data based on microwave and thermal infrared remote sensing images to assess the stability of the tower foundation area where the power transmission and transformation equipment is located, obtaining landslide level distribution data to determine control measures. This invention solves the problem of low accuracy in landslide detection in existing technologies.

This invention discloses a method for identifying rice-growing areas based on multi-temporal optical and polarimetric radar remote sensing images, belonging to the field of agricultural remote sensing technology. The invention includes: S1. Dataset construction, S2. Data preprocessing, S3. Data partitioning, S4. Model construction, S5. Identification step, S6. Re-acquisition step, S7. Model transfer, and S8. Re-identification step. By fusing multi-temporal and multi-source remote sensing image information, the model can effectively identify rice-growing areas. Compared with traditional supervised classification methods and deep convolutional neural network semantic segmentation algorithms, this model shows significant advantages in classification performance. Furthermore, the model also exhibits good cross-year generalization performance, achieving high recognition accuracy not only in the training year but also obtaining good interpretation results in other years. Moreover, the rice-growing fields in the classification map show a continuous distribution, which is more consistent with the actual situation.

This invention discloses a multimodal remote sensing inversion method and system for forest tree height based on bias correction, belonging to the field of forest tree height inversion technology. The method first acquires data from UAV lidar, spaceborne lidar, optical and radar remote sensing, topographic and meteorological data, and performs preprocessing and feature extraction. By constructing a circular buffer based on the geometric dimensions of the spaceborne laser spot and using the median of the UAV canopy height as the ground truth, accurate spatial matching between spaceborne and UAV data is achieved. A machine learning bias correction model integrating multiple terrain features such as slope, aspect, elevation, and mountain shadow is constructed to correct nonlinear systematic errors in the spaceborne lidar data. The corrected spaceborne lidar samples and multimodal remote sensing features are fused and trained using a random forest model to generate a high-resolution, spatially continuous forest tree height distribution map. This invention significantly improves the accuracy and reliability of forest tree height inversion.

This invention discloses an intelligent extraction and assessment method for ecological factors of musk deer habitat based on multi-source image fusion, belonging to the field of ecological remote sensing monitoring and intelligent assessment technology. It addresses the problems of inconsistent multi-source extraction of ecological factors and insufficient reliability of suitability assessment results for musk deer habitat. The method acquires optical remote sensing images, radar remote sensing images, UAV images, topographic data, meteorological data, vector geographic data, and ground survey data within the assessment area. After unified preprocessing, ecological factors such as vegetation, topography, water resources, climate, land cover, and human disturbance are extracted. Then, through multi-source fusion, sample matching, suitability probability calculation, key factor extraction, and result verification and correction, the suitability level of musk deer habitat and the contribution of ecological factors are obtained.

This application relates to radar remote sensing data processing technology, and in particular to an automatic unwrapping method for InSAR based on coherence hierarchical constraints. The method includes: acquiring an interferometric wrapped phase map and a coherence coefficient map; extracting coherence features to generate a coherence hierarchy map containing high, medium, and low-coherence regions; performing initial unwrapping based on the coherence hierarchy map and recording the propagation source, constructing a coherence hierarchy propagation map with propagation risk weights; detecting propagation anomalies using closed-loop errors, boundary cycle slip abrupt changes, and abnormal intrusions in high-coherence regions; when anomalies are detected, tracing the erroneous path back to a high-coherence confidence anchor point, performing a backtracking process, and clearing integer cycle slips on the erroneous path; re-selecting candidate integer cycle slip combinations under confidence anchor point constraints for local re-unwrapping, and updating the final unwrapped phase map and confidence marker map. This application can effectively solve the problem of misjudgment spreading from low-coherence regions to high-coherence regions, improving the stability and reliability of the unwrapping results.

The application discloses a black land degradation intelligent evaluation method and system based on multi-source remote sensing images, relates to the technical field of land degradation intelligent evaluation, and realizes intelligent evaluation through multi-source remote sensing data fusion and a double-branch attention deep convolutional neural network; the method first carries out pretreatment such as radiation correction and geometric registration on optical, thermal infrared and radar remote sensing images, and constructs a multi-dimensional data set containing time-series spectra, textures and ground surface temperatures; then key features are extracted in parallel through a spatial branch and a time-series branch, and adaptive weighted fusion is carried out, and finally, a pixel-level degradation grade map and a change trend report are output; the system can automatically, widely and at low cost complete dynamic monitoring of black land degradation, has high evaluation precision and strong timeliness, and provides reliable technical support for accurate protection of black land and improvement of farmland quality.

The application relates to the technical field of radar remote sensing and target detection, and particularly relates to a multi-scale SAR target detection method and system based on phase consistency constraint, which comprises the following steps: acquiring a SAR image, wherein the SAR image contains multi-polarization channel information; performing feature extraction on the SAR image through a multi-scale feature extraction network to obtain multi-scale features, and introducing a phase consistency constraint in the feature extraction process; enhancing the multi-scale features by using a polarization-enhanced attention mechanism; processing the enhanced multi-scale features through multiple detection heads to output a target detection result. The application aims to provide a multi-scale SAR target detection method and system based on phase consistency constraint, so as to solve the problems of phase response inconsistency in the multi-scale feature transmission process, insufficient utilization of polarization information, fixed feature fusion mode and the like in the prior art, thereby improving the detection accuracy and robustness of targets in a SAR image, especially small targets.

The application discloses a SAR image filtering method based on structure strength joint consistency, and relates to the technical field of radar remote sensing image processing methods.The method comprises the following steps: acquiring double-time-phase SAR images of the same ground surface monitoring area, performing radiation correction and spatial registration to form a double-time-phase SAR image pair to be processed; respectively calculating structure consistency components and strength consistency components of the double-time-phase SAR image pair, and adopting a product strategy to construct a structure-strength joint consistency coefficient; calculating a spatial self-adaptive filtering weight based on the structure-strength joint consistency coefficient, and automatically adjusting filtering strength of flat areas and edge areas; and adopting a consistency-guided self-adaptive weighted fusion filtering to output a final denoising image.The method can capture real geometric features, effectively distinguish the features, and avoid edge blurring and texture loss.

This invention discloses a UAV data acquisition system based on hyperspectral remote sensing and lidar remote sensing. It includes a flight control module for managing the UAV's flight, including navigation, automatic takeoff and landing, path planning, and remote control operation; a hyperspectral sensing module for acquiring high-resolution image data in the visible and infrared spectral ranges to capture spectral information of surface features; and a lidar sensing module for measuring terrain height and three-dimensional structure data of ground features, including ground elevation and object location. This invention, by combining UAVs with hyperspectral remote sensing and lidar technologies, can provide high-precision geographic information, improve data acquisition efficiency, and achieve high-precision registration and fusion of multi-source data. It has advantages such as high automation, outstanding intelligence, and wide applicability, and can be widely applied in fields such as agricultural monitoring, environmental assessment, urban planning, and resource surveys.

This invention provides a remote sensing method for identifying seasonal changes in glaciers, relating to the field of radar remote sensing image processing technology. The method includes: S1: acquiring remote sensing images of glaciers in different seasons within the study area, forming a remote sensing image set; S2: preprocessing the remote sensing image set to obtain a preprocessed remote sensing image set, and then performing data registration on the preprocessed remote sensing image set to obtain a registered remote sensing image set; S3: performing image adjustment, boundary extraction, and seasonal inversion on the registered remote sensing image set to obtain a set of glacier boundary parameters; S4: analyzing the glacier boundary parameter set through STL decomposition to obtain the results of seasonal changes in the glacier. This invention introduces seasonal analysis into glacier analysis, considering the influence of various factors on the data, thus improving the stability of identifying seasonal changes in glaciers.

The application relates to the technical field of radar remote sensing, in particular to a ground surface deformation monitoring method, device and system based on InSAR, which comprises the following steps: acquiring multi-source monitoring data covering a target region; dividing the target region into multiple sub-regions; for each sub-region in a synthetic aperture radar image of each time phase, determining the instant terrain interference degree of the sub-region according to the multi-source monitoring data; for each sub-region, determining the terrain interference comprehensive index of the sub-region in each time phase according to the instant terrain interference degrees of the sub-region in all time phases; mapping the terrain interference comprehensive index of each sub-region to the phase quality weight of each sub-region, and performing phase unwrapping processing on the multi-time-phase synthetic aperture radar image based on the phase quality weight of each sub-region to obtain the ground surface deformation monitoring result of the target region. The application improves the InSAR ground surface deformation monitoring precision and reliability in a complex terrain region.

The application discloses a multi-modal disaster space-time graph construction method, system, device and storage medium, and relates to the technical field of intelligent water conservancy. The method comprises the following steps: a physical evolution disaster chain field ontology is constructed by relying on a basin water system topology; unstructured disaster text is subjected to zero-sample element extraction, absolute geographic coordinates and high-frequency time stamps are bound as space-time attribute, and micro-dynamic disaster point elements are generated; water body range vectors are extracted based on radar remote sensing images and are vectorized as macro-physical envelope surface elements; under a unified geographic information system, a space-time tolerance alignment mechanism of macro-surface constraint and micro-point interpolation is adopted, a spatial physical buffer tolerance is set, a cross-modal physical trust edge is established when the space-time constraint is met, and finally, a graph is constructed and cross-scale deduction is performed. The application solves the problems that the existing disaster graph is separated from the physical space mapping, the traditional extraction model is prone to chain breakage, and the multi-modal data space-time matching is difficult, and improves the knowledge extraction accuracy.

The application discloses a UAV-borne MiniSAR real-time imaging and emergency communication system for disaster relief, relates to the technical field of emergency communication and radar remote sensing, and comprises a radar path inversion modeling module, which performs multi-time flight over a target area, collects radar echo signals at multiple times, simultaneously acquires flight attitude data and imaging azimuth information of a flight platform, inverts the propagation path of the radar echo signals based on the flight attitude data and the imaging azimuth information, extracts the incident angle and the reflection path length, and generates a reflection path distribution diagram.The application can accurately identify and eliminate false target points under multi-path interference through multi-time radar path modeling and image stability evaluation, realize image structure repair by combining a dynamic extension algorithm, construct a dynamic confidence distribution diagram, realize high-confidence target positioning and rescue area optimization, and enhance the anti-interference and response accuracy of the system.

The present application relates to the technical field of radar remote sensing monitoring and soil and water conservation, and discloses a basin soil erosion intelligent sensing and early warning system based on space-air-ground cooperation, comprising: a synthetic aperture radar observation platform, a plurality of variable-depth active coherent beacons arranged on the surface of a monitored basin, and a data processing and inversion center; the synthetic aperture radar observation platform is used for constructing an air-ground active observation link of downlink radar pulse irradiation and uplink echo reception; the variable-depth active coherent beacon comprises a deep anchoring unit and a surface floating unit, and is used for capturing a surface vertical displacement difference value and mapping the displacement difference value into a baseband phase modulation amount by using a digital gain coefficient greater than 1. By introducing orthogonal coding modulation technology in the slow time domain and combining a matching projection operator based on a precise orbit state vector, accurate decoupling of the beacon echo under a strong ground clutter background is realized.

This invention relates to the fields of radar remote sensing and edge computing technology, specifically to an edge computing system and method for fine-grained monitoring of insects and birds based on weather radar IQ data. The system is deployed at a weather radar station and directly acquires raw complex IQ data. The system includes: an edge IQ data preprocessing module for clutter suppression and time-frequency domain transformation; a multi-dimensional biofeature extraction module for extracting micro-Doppler features, dual-polarization parameter features, and radar cross-section features; a cascaded classification and identification module using a two-stage classifier to filter non-biogenic echoes and distinguish between insects and birds; a spatiotemporal trajectory tracking module for obtaining target trajectories; a biomass inversion and product generation module for inverting biomass density and generating structured monitoring products; and an edge cloud collaboration module for uploading products to the cloud. This invention directly processes raw complex IQ data through an edge computing architecture, preserving fine features such as micro-Doppler, to achieve fine-grained identification, real-time monitoring, and quantitative analysis of insect and bird targets.

This invention provides a remote sensing monitoring method and system for desertification control projects, belonging to the field of desertification monitoring technology. The method includes: acquiring and preprocessing optical and radar remote sensing images based on the spatial heterogeneity of the desert; determining dynamic target control information by coupling desert ecological evolution laws with the growth cycle of psammophytes; obtaining actual control information through multi-source data fusion and inversion, and determining real-time project progress by combining dynamically calculated control effectiveness attenuation coefficients; obtaining simulated control progress with environmental confidence intervals through a wind-sand-precipitation coupled spatiotemporal sequence prediction model; and correcting the real-time progress with dynamic environmental sensitivity coefficients to obtain accurate control progress. This achieves dynamic and accurate monitoring throughout the entire process, and is adapted to the desert environment, improving the matching degree between monitoring results and project progress and environmental changes.