217 results about "Digital surface" patented technology

The invention relates to the field of medical robots, and discloses a meridian point foundation construction method for precise positioning of an acupuncture robot, which comprises the following steps: scanning skin texture, muscle contour and skeleton mark features of a human body surface, and establishing a human body surface feature digital model; the method comprises the following steps: acquiring temperature distribution data of a human body surface, human body surface feature digital model data and elastic modulus data, performing weight mapping feature level fusion to generate an enhanced acupuncture point feature map, and establishing an acupuncture point-meridian association relationship matrix based on the enhanced acupuncture point feature map; compared with the prior art, the method has the advantages that three-dimensional body surface scanning, infrared thermal imaging and elastic modulus data are integrated through the multi-modal data fusion technology to generate the enhanced acupoint feature map, the dynamic meridian-acupoint twinborn model is constructed in combination with double-ellipse section fitting and implicit curved surface reconstruction, and tissue deformation is simulated in real time.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention provides a lodging area determination method and system based on a lodging monitoring spectral index image, and relates to the field of crop form prediction.The method comprises the steps that firstly, a target farmland remote sensing image and a digital surface model are obtained, radiation and geometric correction are completed in combination with GNSS positioning data and meteorological data, and a standardized orthoimage is generated; calculating a vegetation index on the image and carrying out difference to obtain change information; executing opening and closing operation by adopting a structural element adaptive to the image resolution and the crop row spacing, filtering noise according to the area of a connected domain or a pixel number threshold value, and extracting a spectral index abnormal region and a boundary thereof; and then fusing the boundary and the digital surface model under a unified coordinate reference, accumulating the area according to the pixel resolution, and outputting the area of the abnormal region. The method has parameter self-adaption and multi-source data fusion capabilities, can stably obtain a consistent abnormal region area in a multi-resolution and complex field environment, and provides reliable technical support for agricultural condition monitoring and disaster assessment.

The invention belongs to the technical field of glacier identification, and provides a system for monitoring glacier dynamics by an unmanned aerial vehicle low-altitude flight photogrammetry method, which comprises the following steps that: a glacier area unmanned aerial vehicle low-altitude photogrammetry module selects an unmanned aerial vehicle as required, and plans a route and parameters; the image preprocessing module is used for extracting key feature points from each image by using Pi * 4Dcap; fine three-dimensional information, an ortho-image and a glacier digital surface model DSM are obtained through point cloud generation and densification, and glacier dynamic information can be identified by utilizing the glacier digital surface model DSM after correction and interpolation processing are carried out on the three-dimensional information, the ortho-image and the glacier digital surface model DSM; calculating the elevation, volume, speed and mass change of the glacier based on a digital surface model (DSM) of the glacier; the glacier fissure is identified by utilizing the orthoimage and the glacier digital surface model DSM, and the system can comprehensively and accurately monitor the dynamic information of the glacier.

The invention relates to an urban live-action three-dimensional construction method based on a high-resolution satellite image. The method comprises the following steps: carrying out stereo image pair screening and stereo image pair preprocessing on a high-resolution multi-view satellite image; carrying out adjustment optimization based on a rational function model on the satellite stereo image pair; generating an epipolar ray image based on the optimized rational function model; performing dense matching on the epipolar line image by combining building edge straight line segment constraint and an MGM algorithm to generate a disparity map; generating a three-dimensional point cloud and a three-dimensional model formed by a plurality of stereo image pairs through forward intersection based on the disparity map and the optimization parameters; generating a digital elevation model, a digital surface model and a three-dimensional Mesh model based on the three-dimensional point cloud; and carrying out precision evaluation and integrity evaluation by adopting pixel proportions of plane precision, elevation precision and height difference. According to the method, the urban live-action three-dimensional model and the corresponding digital product can be constructed quickly, efficiently and accurately by using the high-resolution satellite image.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The invention provides an urban area-oriented multi-source remote sensing data joint positioning method and system, and the method comprises the steps: obtaining a plurality of optical stereoscopic images and corresponding connection point data, constructing an optical stereoscopic block, and extracting laser elevation control point data and digital surface model data in the range of the optical stereoscopic block; calculating a ground coverage range of a plurality of optical stereoscopic images, mapping the ground coverage range to a grid unit by adopting a grid lookup table, obtaining a laser point accurate image space coordinate by combining the screened laser elevation control point data, and outputting a laser elevation control point set; calculating a laser elevation control point set based on a rational function model and affine transformation, and constructing a multi-source remote sensing data joint adjustment function model; and resolving the multi-source remote sensing data joint adjustment function model to form an observation error equation, performing heuristic search solution on the observation error equation, and outputting an adjustment model parameter result and a final connection point ground coordinate.

The invention discloses a field-ditch-pond extraction method based on unmanned aerial vehicle photogrammetry and deep learning, and belongs to the technical field of remote sensing technology and agricultural informationization, and the method comprises the steps: employing an unmanned aerial vehicle to carry a multispectral camera and an optical lens, and obtaining original image data through an oblique photogrammetry technology; preprocessing the obtained original image data to generate a multispectral image map (DOM) and a digital surface model (DSM); starting from the spectrum, terrain and texture features of a small watershed field-ditch-pond system, a U-net algorithm with a built-in attention mechanism, an image segmentation model supporting zero sample learning, a multi-scale segmentation technology and a multiple extraction framework of a shape feature extraction algorithm are constructed, image segmentation, classification and interpretation are carried out, and a multi-scale image segmentation model is constructed. Fine extraction of the small watershed field-ditch-pond system is realized; the design can improve the extraction precision and efficiency under complex terrains, and is suitable for current small watershed agricultural non-point source pollution migration trajectory simulation and farmland fine management.

The invention discloses an island shoreline erosion monitoring method and system based on image recognition, and the method comprises the steps: processing an optical image through meteorological data, obtaining a clear optical image, carrying out the multi-source feature fusion of the clear optical image and an SAR image, and obtaining a preliminary shoreline probability graph; carrying out fine extraction and image recognition on the initial shoreline probability graph according to the instantaneous tide level to obtain an island shoreline planar graph, carrying out image registration and constructing an island shoreline digital surface model, calculating a three-dimensional deviation field and quantifying an erosion index, calculating a driving factor, and determining a dynamic monitoring threshold according to the driving factor and a standard monitoring threshold. And carrying out island shoreline erosion early warning according to the erosion index and the dynamic monitoring threshold. The method not only can improve the efficiency and accuracy of island shoreline erosion monitoring, but also has good interpretability, and can be directly applied to an island shoreline erosion monitoring system.

The invention discloses an adjacent foundation pit deformation prediction method for a dense building group and a support deformation prediction method, and the method comprises the steps: obtaining SAR satellite images of a to-be-processed region in a period of time sequence, and generating an image stack after registration; screening points of which the amplitude deviation indexes are lower than a threshold value as PS points; calculating the relative displacement sequence of each PS point, inverting the displacement variation and accumulating to obtain the accumulated settlement; before construction, a digital surface model (DSM) is generated through aerial survey of an unmanned aerial vehicle, feature points with stable displacement are recognized by combining SAR images, and the feature points are anchored to an absolute coordinate system of the unmanned aerial vehicle for correction. Therefore, permanent scatterer (PS) identification and time sequence analysis are carried out by utilizing SAR images of a long time sequence, absolute geographic reference is provided by a high-precision digital surface model (DSM) generated by aerial survey of an unmanned aerial vehicle, and reference correction is carried out on a relative displacement field acquired by the InSAR.

The invention discloses a three-dimensional terrain construction method based on multi-modal semantic constraints, and relates to the technical field of three-dimensional terrain modelling, and the method comprises the steps: obtaining an optical remote sensing image and a digital earth surface model, constructing unified space reference data, and carrying out the joint semantic analysis of ground features under the guidance of the multi-modal semantic constraints; generating a semantic mask set with structural consistency; further performing regional geometric reconstruction on the digital earth surface model on the basis of a mapping relation between semantic categories and earth surface physical attributes to form a three-dimensional terrain entity conforming to ground feature space expression characteristics; meanwhile, by analyzing the project cost document and extracting the project cost data with spatial directivity, under the constraint of spatial consistency and semantic consistency, the project cost information and the three-dimensional topographic entity are subjected to association mapping, and finally a three-dimensional topographic data result fusing topographic geometric information and project economic attributes is formed.

A method of facilitating the landing of a civil aircraft on a potential landing zone divided into sub-zones. The method includes a step of producing a digital surface model of the potential landing zone using topographic terrain data. In a placement step, the digital surface model is positioned and / or orientated in space to obtain a placed digital surface model. In a calculation step, an overall risk level is calculated for each sub-zone on the basis of the placed digital surface model and characteristics of the sub-zone including topography, accessibility, loss of visibility, and presence of living beings. A reporting step comprises generating an information signal dependent on the overall risk levels, and / or transmitting a control signal dependent on the overall risk levels.

The invention relates to a modeling method, in particular to an unmanned aerial vehicle spectral image BRDF rapid acquisition and adaptive generalization modeling method. The method comprises the following steps: 1, determining a research area, obtaining multi-angle BRDF data of the research area, and obtaining illumination change data at the same time; 2, preprocessing the acquired multi-angle BRDF data in combination with illumination change data to obtain preprocessed multi-angle images, splicing the multi-angle images to generate a panorama of the research area, and acquiring a digital surface model graph at the same time; 3, carrying out pixel-by-pixel classification on the research area based on the panorama to obtain a ground feature category map; calculating the gradient and the slope direction of the research area pixel by pixel based on the digital surface model graph; 4, correcting a preset BRDF model by using the acquired illumination change data, gradient and slope direction; and 5, self-adaptive generalization modeling is carried out. According to the method, high-efficiency BRDF acquisition can be realized, and the influence of regional change, space environment and topographic factors on BRDF modeling precision can be eliminated.

The invention relates to the technical field of unmanned aerial vehicle navigation, and relates to an unmanned aerial vehicle inspection path planning method, system and device and a storage medium. The unmanned aerial vehicle inspection path planning method comprises the following steps: acquiring a digital elevation model and a digital surface model of a plateau forest region and laser point cloud data of the plateau forest region; determining a three-dimensional voxel space of the plateau forest region based on the digital elevation model, the digital surface model and the laser point cloud data, performing reachability judgment on the three-dimensional voxel space based on the vertical safety margin, and determining a reachable region of the unmanned aerial vehicle; carrying out three-dimensional path search from a starting point to a terminal point by using an improved algorithm in an unmanned aerial vehicle reachable area; and smoothing the searched three-dimensional path, and applying a maximum turning radius constraint and a maximum climbing rate constraint to generate a final three-dimensional track. According to the invention, the safety and the performability of route planning of the unmanned aerial vehicle can be improved.

The invention provides a terrain and ground feature construction system and method based on satellite remote sensing and unmanned aerial vehicle aerial photography, and the method comprises the steps: screening an aerial photography region from a target region according to a ground feature classification initial image of the target region, and carrying out the multi-view dense matching of a multi-view aerial photography image sequence of the aerial photography region, generating a three-dimensional grid model and a second digital surface model of the aerial photography area; performing feature fusion on the first digital surface model and the second digital surface model based on registration constraints of a terrain surface of a target area, and constructing a multi-scale digital surface model of the target area; and performing texture mapping integration on the three-dimensional grid model and the multi-scale digital surface model, outputting a multi-level real scene three-dimensional model of the target area, and completing terrain and ground feature integrated construction of the target area. On the basis of the scheme, advantage complementation of satellite wide-area perception and unmanned aerial vehicle accurate exploration can be realized, so that the integration level of a live-action three-dimensional model in macroscopic coverage and microscopic detail precision can be improved.

The invention relates to the field of disease control, and discloses a semantic segmentation and grading method for cross-scale identification of rice black dwarf disease and lodging, and the method comprises the following steps: S1, collecting rice field data under different elevation scales, and processing the data through software to obtain image data; s2, based on the altitude information of the DSM, determining a classification threshold by combining elevation error correction and height distribution difference statistical analysis, and constructing a DSM-guided auxiliary supervision mask; s3, constructing a cross-scale semantic fusion network, and inputting the image data set in S1 and the auxiliary supervision mask in S2 into the network for training to obtain a rice black dwarf disease and lodging detection model; and S4, predicting by using the rice black dwarf disease and lodging detection model in the S3. By constructing a digital surface model and a digital orthoimage with aligned pixels and combining elevation error correction, accurate association of rice field data under multiple elevation scales can be realized, and support is provided for rice black dwarf disease and lodging identification.

The application discloses a kind of unmanned aerial vehicle photogrammetry and depth learning's field-gully-pond extraction method, belong to remote sensing technology and agricultural informatization technical field, method includes: using unmanned aerial vehicle carries multispectral camera and optical lens, obtains original image data by tilt photogrammetry technique;The original image data obtained is preprocessed, generates multispectral image (DOM) and digital surface model (DSM);From the spectral, topography and texture features of small watershed field-gully-pond system, construct the multiple extraction framework of U-net algorithm with built-in attention mechanism, image segmentation model supporting zero sample learning, multi-scale segmentation technology and shape feature extraction algorithm, carry out image segmentation, classification and interpretation, realize the fine extraction of small watershed field-gully-pond system;The design can improve the extraction accuracy and efficiency under complex terrain, applicable to current small watershed agricultural non-point source pollution migration trajectory simulation and farmland fine management.

A digital surface position sensor includes a position sensor, an adaptable hardware interface, and a processing circuit. The position sensor is adapted to be coupled to an aircraft flight control surface and is configured to sense a position of the aircraft flight control surface and supply a position signal representative thereof. The adaptable hardware input supplies an identification signal that identifies the aircraft flight control surface to which the position sensor is coupled. The processing circuit is coupled to receive the position signal and the identification signal. The processing circuit is configured, upon receipt of the signals, to process the position signal and the identification signal and generate (i) a first digital position signal representative of the position of, and the identification of, the aircraft flight control surface and (ii) and independent second digital signal representative of the position of, and the identification of, the aircraft flight control surface.

The invention provides a multi-source remote sensing fusion method and system based on visible light images and SAR (Synthetic Aperture Radar) images. The multi-source remote sensing fusion method comprises the following steps: acquiring a visible light image pair and an SAR image pair of a target area in the same time and space; carrying out feature matching and orientation on the visible light image pair to obtain an oriented image pair, carrying out dense matching on the oriented image pair by utilizing a pyramid matching strategy in combination with a semi-global algorithm to obtain a disparity map, and extracting visible light point cloud from the disparity map; carrying out dense matching on the SAR image pair by utilizing object space elevation search to obtain an SAR point cloud; the method comprises the following steps: generating a multi-source digital surface model by performing point cloud rasterizing interpolation on visible light point cloud and SAR point cloud, unifying coordinates and elevations of the multi-source digital surface model, and performing multi-source elevation fusion to obtain a fused digital surface model; and performing point cloud conversion on the fusion surface model to obtain a three-dimensional point cloud of the target area, thereby realizing accuracy and generalization of multi-source data fusion.

The invention provides a three-dimensional image modeling method and device based on plane data, and the method comprises the steps: carrying out the full-color / multispectral band fusion, bit depth adjustment, thin cloud removal, image enhancement, stripe removal and light and color uniformity of a satellite surveying and mapping image, and achieving the real color recovery of the image; performing splicing preprocessing on the plurality of small orthographic satellite surveying and mapping images after real color recovery, performing image registration on the to-be-registered image and a reference image, performing image geometric correction on the registered image, and performing image mosaic processing on the plurality of images after image geometric correction to obtain a single large-scene image; and three-dimensional image matching, building mask extraction, three-dimensional building modeling and three-dimensional building post-processing are carried out on a single large scene image to obtain a three-dimensional digital surface model, and ground three-dimensional real image modeling is completed. By applying the technical scheme of the invention, the technical problem that the existing two-dimensional surveying and mapping data is difficult to meet the increasing application requirements of three-dimensional scenes is solved.

The invention discloses a mountainous area surface matrix physical property prediction method, and relates to the technical field of geological exploration, and the method comprises the steps: obtaining mountainous area surface point cloud data, separating vegetation from ground points through a point cloud classification algorithm, and generating a digital elevation model and a digital surface model; based on the digital elevation model and the digital surface model, a quantum gravity gradiometer is adopted to move along a contour line for measurement, a gravity gradient tensor is obtained, terrain interference signals are eliminated, and a vertical density profile is obtained through inversion; identifying a density abnormal region according to the vertical density profile, acquiring an undisturbed soil column sample by adopting a drilling unit, determining a grain composition and moisture characteristic curve, performing DNA extraction and microorganism sequencing on the undisturbed soil column sample, and calculating the relative abundance of the nitro spirillum phylum. According to the method, the dynamic coupling model is constructed through fusion of quantum gravity gradient measurement and microbiological omics data, and the problem of insufficient vertical density-permeability coefficient coupling modeling precision of a traditional method is solved.

The invention discloses a digital surface model (DSM)-guided hyperspectral and LiDAR combined unmixing method, relates to the field of multi-modal image processing, and aims to solve the problems of end member confusion and insufficient space structure maintenance caused by spectrum similarity in hyperspectral unmixing. According to the method, a hyperspectral image and LiDAR data of the same area are obtained, a LiDAR elevation map is expanded into a multiband profile through an attribute configuration file method, and a digital surface model (DSM) is generated. Constructing a spectrum and space double-branch auto-encoder, respectively extracting spectrum and space features and carrying out fusion mapping, obtaining an abundance matrix by using a normalized exponential function, and reconstructing a hyperspectral image; a double-branch adaptive mixed channel attention mechanism is designed in a spectrum branch, and a space attention mechanism is introduced in a space branch. In the training process, a self-defined loss function is formed by combining DSM-guided structure entropy regularization, spectral angular distance and root-mean-square error, and the space continuity and boundary retention of the abundance graph are improved. The method is suitable for hyperspectral unmixing and multi-modal remote sensing fine identification under complex terrains.

The invention discloses a tree positioning calculation method based on unmanned aerial vehicle remote sensing, and relates to the technical field of agriculture and forestry vegetation positioning. According to the method, firstly, a digital orthoimage and a digital earth surface model (DSM) are generated through aerial photography of an unmanned aerial vehicle, a crown mask is obtained by using an instance segmentation technology, and a trunk position is accurately positioned by calculating a fusion score in the mask by using a joint resolving mechanism of a DSM local peak domain and an iterative corrosion morphological core. The method improves the positioning precision and efficiency through a DSM highest point * iterative corrosion combined optimization strategy, and improves the positioning stability of dense crowns through the corrosion depth constraint space position. According to the method, the positioning precision and efficiency of a complex forest environment are remarkably improved, and the problems of insufficient positioning precision, difficulty in positioning trunks under dense crowns and the like in the existing unmanned aerial vehicle remote sensing technology are solved.

The invention relates to the technical field of urban hydrological simulation. The invention discloses an urban community surface ponding simulation method considering a roof structure, and the method comprises the steps: obtaining a regional multi-angle picture through an oblique photography unmanned plane, generating an oblique photography model through image processing, and reconstructing a digital surface model according to the oblique photography model; ground data are separated from the DSM through a PMF method, roof data are extracted in combination with building features, mask data are output, other ground features are removed, and building roof elevation grid data are obtained; calculating the gradient of the roof, dividing the gradient into flat and sloping roofs, building drainage structures into roof drainage models, integrating the roof drainage models with the urban community hydrological model considering the roofs, and running to realize surface ponding simulation. The method comprises the following steps: building a high-precision model through an oblique photography unmanned aerial vehicle and a GIS, separating ground data through a PMF, and accurately dividing a flat / slope roof through median filtering and an SVM; special drainage structures are arranged for the two types of roofs; and integrating the roof drainage model and a two-dimensional coupling hydrological model, and outputting an accumulated water thematic map.

The invention provides a scene construction method and system based on micro-renderable multi-source remote sensing data, and the method comprises the steps: obtaining multi-source remote sensing data, namely, high-resolution remote sensing images and laser radar data; rendering primitive distribution is initialized by reducing a sampling point cloud, a rendering depth constraint function is established by using a point cloud projection depth map, and a primitive space distribution priori field is constructed based on local neighborhood features of the point cloud; a multi-scale progressive training strategy is designed, an image pyramid structure is adopted to realize resolution adaptive optimization, and performance overhead is reduced in combination with an image block asynchronous optimization mechanism; and rendering a scene true shoot and a digital surface model by using orthogonal projection, and synchronously realizing laser radar point cloud color assignment. According to the method, a new multi-modal remote sensing data collaborative modeling mechanism is provided, joint optimization of geometric-spectral features is realized through a micro-renderable architecture, and a complex geographic scene construction task can be dealt with.

The present application relates to the technical field of unmanned aerial vehicle navigation, and relates to an unmanned aerial vehicle inspection path planning method, system, device and storage medium; wherein the unmanned aerial vehicle inspection path planning method comprises: obtaining a digital elevation model, a digital surface model of a plateau forest area and laser point cloud data of the plateau forest area; determining a three-dimensional voxel space of the plateau forest area based on the digital elevation model, the digital surface model and the laser point cloud data, and determining an unmanned aerial vehicle accessible region by performing accessibility determination on the three-dimensional voxel space based on a vertical safety margin; performing three-dimensional path search from a starting point to an ending point in the unmanned aerial vehicle accessible region by using an improved algorithm; performing smoothing processing on the searched three-dimensional path, and imposing a maximum turning radius constraint and a maximum climb rate constraint to generate a final three-dimensional track. The present application can improve the safety and executability of the planned route of the unmanned aerial vehicle.

The invention provides a method for quickly checking an oblique photography model, and relates to the technical field of image processing. Comprising the following steps: S1, acquiring an oblique photography model, a digital orthoimage, a digital surface model and high-precision control point data, judging a topographic type of a measured area based on DSM elevation mutation parameters, and correspondingly executing format conversion, noise removal and hierarchical data registration to form a standardized auditing data set adapted to topographic features; s2, based on the preprocessed data set, synchronously executing time-space cooperation precision detection and feature fusion integrity and compliance detection, and introducing a three-dimensional evaluation model to complete dynamic risk level evaluation; s3, generating a visual auditing report containing a dynamic label; the problems of complex terrain adaptation, risk assessment and closed-loop efficiency in the prior art are solved, the multi-dimensional detection coverage rate is increased, the risk identification accuracy is improved, the auditing and rectification closed-loop efficiency is improved, the labor cost is reduced, and the scene adaptability is improved.

The invention discloses a morphological filtering iteration-based digital elevation model generation method. The method comprises the following steps of: 1) partitioning an image; 2) morphological grayscale filtering; (3) iterative binarization is carried out; 4) marking connected domains; 5) threshold filtering; 6) surface interpolation; and 7) merging processing. According to the method, morphological gray filtering and iterative threshold segmentation technologies are fused, a block optimization strategy and a dynamic connected domain screening mechanism are combined, and the problems that a traditional algorithm is sensitive in window size, poor in terrain adaptability, multiple in set parameters and the like are effectively solved. And the automatic processing efficiency of generating a digital elevation model (DEM) by a digital surface model (DSM) under a complex terrain and the terrain restoration precision are obviously improved.

The application provides a multi-source remote sensing fusion method and system based on visible light images and SAR images, comprising: acquiring a visible light image pair and a SAR image pair of a target region in the same space-time; performing feature matching and orientation on the visible light image pair to obtain an oriented image pair; performing dense matching on the oriented image pair by using a pyramid matching strategy combined with a semi-global algorithm to obtain a disparity map; extracting a visible light point cloud from the disparity map; performing dense matching on the SAR image pair by using object elevation search to obtain a SAR point cloud; generating a multi-source digital surface model by performing point cloud gridding interpolation on the visible light point cloud and the SAR point cloud; after unifying coordinates and elevations of the multi-source digital surface model, performing multi-source elevation fusion to obtain a fused digital surface model; and performing point cloud conversion on the fused surface model to obtain a three-dimensional point cloud of the target region, so that the accuracy and generalization of multi-source data fusion are realized.