263 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.

According to the cultural relic mural three-dimensional digital protection and micro-change detection method, non-contact and damage-free image data acquisition and calculation are performed on the mural collection wall painting by using a digital photogrammetry correlation technology, and an orthographic image DOM and a digital surface model DSM of the mural collection wall painting are obtained. Color management is implemented in the links from data acquisition, calculation to result display, so that the color of the obtained digital orthographic image of the mural is consistent with the color of the actual mural. And carrying out fresco disease change analysis detection on the obtained fresco orthoimage and the digital surface model in a combined manner, obtaining the position and the area of the disease change, identifying the type of the disease, and finally outputting a fresco micro-change detection result extraction image. Scientific detection means and evaluation methods are provided for mural protection, and the efficiency of mural protection work is greatly improved. Meanwhile, detection data are analyzed, the change rule of the mural is mastered, the micro change trend of the mural is predicted, and the mural is effectively protected.

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 discloses a building roof elevation extraction method and system based on city modeling, and the method comprises the steps: S1, point cloud data and image data fusion registration: extracting data features, and carrying out alignment registration; s2, roof denoising and boundary optimization: identifying interference information, extracting a roof boundary, and generating a roof digital surface model through interpolation; s3, roof elevation extraction: for a flat roof, extracting the top elevation of the flat roof through a plane fitting method; for the slope top roof, extracting the top elevation of the slope top roof through an extreme point detection method; and extracting the top elevation of the roofs except the flat roofs and the slope roofs through a point cloud clustering method. According to the scheme, the roof elevation can be rapidly and accurately extracted, and high-precision three-dimensional data support is provided for urban planning, energy assessment and disaster emergency; the problems that in the prior art, roof elevation extraction efficiency is low, complex roof structure recognition precision is insufficient, and multi-source data fusion is difficult are solved.

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.

Provided are a method of individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization. First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied to accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach using seven diverse plots in the subtropical Gaofeng Forest, China, coupled with ground verification. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marked-control watershed method, especially in complicated intersections of multiple crowns.

The invention discloses an unmanned aerial vehicle variable-speed aerial photography method and device for multi-class ground feature elevation and a medium, and relates to the technical field of photogrammetry, and the method mainly comprises the steps: taking the texture of each direction of a static target as an information collection demand, and generating a cross route with mutually vertical horizontal planes as initial route data; controlling the unmanned aerial vehicle to fly according to the initial route data, and shooting a sequence image at a forward-looking preset angle; integrating the digital earth surface model data and the sequence image, and extracting an elevation data set of each static target in the sequence image; according to the elevation data set, analyzing the elevation distribution proportion change of the static targets in the adjacent sequence images, and carrying out speed adjustment along with the proportion change; and generating final route data according to the speed regulation result, and controlling the unmanned aerial vehicle to perform variable-speed aerial photography according to the final route data. The flight speed of the unmanned aerial vehicle is dynamically adjusted, dense collection of high-rise building images is guaranteed, and redundant data in low areas are avoided.

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.

The invention relates to a lightweight building three-dimensional model extraction method and system based on a convolutional neural network, and the method comprises the steps: constructing a sample library through employing two kinds of remote sensing image data, i.e., a digital orthographic map (DOM) and a digital surface model (DSM), and corresponding building contour and building real height values; and training the convolutional neural network for extracting the three-dimensional model of the lightweight building, and learning two-dimensional and three-dimensional features of the building. The trained network model is used for predicting new DOM and DSM data, and a prismatic lightweight building three-dimensional model can be directly extracted. The method has the advantages that building contour extraction and height estimation are carried out without depending on artificial experience design rules, and the automation degree is high; the efficient reasoning speed of the convolutional neural network can ensure that a lightweight building three-dimensional model in a large-range region can be quickly constructed, and the extraction efficiency is high.

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.

An apparatus for an aerial vehicle is introduced. The apparatus may comprise a camera and a processor coupled to the camera. During flight, the processor may obtain an image from the camera, transform it into a bird-eye view image, and match it with digital surface model (DSM) data. Based on this matched image, the processor may generate a depth map image. Further, the processor may utilize the depth map image to produce a signal for controlling the operation of the aerial vehicle.

The invention discloses an orthoimage generation method and device based on scene reconstruction, and relates to the technical field of image processing. The method comprises the following steps: carrying out coordinate conversion on images, and establishing an adjacency relation between the images; performing feature point matching on the images based on the adjacency relation to obtain a matching pair file, and determining an initial matching pair and poses of other images relative to the initial matching pair in the matching pair file to obtain an object space sparse point cloud; filtering and rasterizing the object space sparse point cloud to obtain a digital surface model; performing point cloud extraction and surface reconstruction on the digital surface model to obtain a surface grid model; performing texture mapping on the surface mesh model to obtain a texture three-dimensional model, and converting the texture three-dimensional model to a target coordinate system to obtain a target three-dimensional model; and performing orthographic projection on the target three-dimensional model to obtain a two-dimensional orthoimage. According to the method, the three-dimensional model can be reconstructed more quickly under the condition that the model precision is ensured.

The invention discloses a method for calculating urban space Beidou satellite visibility by using a virtual panoramic shielding image, and the method comprises the steps: firstly, generating a high-precision digital surface model based on a point cloud; secondly, calculating an initial region of a high-precision digital surface model of the virtual panoramic shielding image, adaptively selecting a range of the high-precision digital surface model used for constructing the virtual panoramic shielding image at any position of the urban three-dimensional space through continuous iteration, and calculating an azimuth angle and an elevation angle according to the range; converting the obtained cylindrical virtual panoramic shielding image into a semi-spherical virtual panoramic shielding image, and projecting the semi-spherical virtual panoramic shielding image into a plane circumference virtual panoramic shielding image; and finally, judging the visibility of the Beidou satellite according to a plane relation graph of the plane circumference virtual panoramic shielding graph and the sky graph of the Beidou satellite. According to the invention, the precision and resolution of the existing urban digital surface model are improved; the data calculation amount is reduced, the calculation efficiency is improved, and the efficiency and the accuracy of urban three-dimensional space Beidou satellite visibility calculation are improved.

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 target detection method and system fusing DOM and DSM data, and belongs to the technical field of target detection, and the method comprises the steps: obtaining original image data, carrying out the preprocessing, and making DOM data of a target region; constructing a digital surface model based on the original image data to obtain DSM data of the target area; performing feature fusion on the DOM data and the DSM data through a graph fusion model to obtain multi-modal fusion data; and making a training set based on the multi-modal fusion data, training a target detection model, and performing building identification through the target detection model. According to the invention, the DOM data and the DSM data of the target area are constructed based on the original image data, and feature fusion is carried out through the graph fusion model to obtain the multi-modal fusion data to carry out building target detection training, so that information complementation is carried out on the data of different modals, the information amount of the data is enriched, and the accuracy of building target identification is improved.