164 results about "Digital surface" patented technology

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 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 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 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 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.

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 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.

This invention relates to the field of laser processing technology, and discloses a method and system for femtosecond laser three-dimensional surface processing based on adaptive optics. The method includes: spatial topology reconstruction of the original point cloud data of the workpiece to be processed to obtain a three-dimensional digital surface; row spacing planning and optimization of the three-dimensional digital surface to obtain a spatial processing trajectory, and point wavefront mapping of the spatial processing trajectory to obtain pre-compensated phase distribution data; optical field distortion analysis of the surface reflected light signal of the workpiece to be processed to obtain the actual wavefront phase; encoding the pre-compensated phase distribution data into an initial wavefront control command for a beam shaping device, and iteratively optimizing the initial wavefront control command to obtain a wavefront supplement signal; and updating the spatial processing trajectory by combining the real-time contour data of the workpiece to be processed. This invention can improve the efficiency of femtosecond laser three-dimensional surface processing based on adaptive optics.

The present disclosure relates to a system and method for improving or refining the accuracy and / or level of detail of a 3D surface based on pixel data from a set of images, such as color data of pixels. In particular, the present disclosure relates to a 3D scanner system comprising an intraoral 3D scanner, comprising: a projector unit comprising a light source and a pattern generating element for structuring light from the light source into a pattern to be projected onto a surface of an object; and two or more cameras configured to acquire a set of images, where the set of images includes images from each camera, where each image includes an array of pixels, each pixel having a pixel color; the scanner system further includes one or more processors operatively connected to the intraoral 3D scanner configured to: generate a three-dimensional (3D) surface of the object based on the set of images obtained from the camera, where the 3D surface includes a plurality of points and / or vertices; and generating a refined 3D surface by solving the optimization problem wherein points and / or vertices in the 3D surface are relocated such that for each point or vertex, a measure based on differences in pixel colors associated with that point or vertex will be minimized.

The invention discloses a coastal zone nitrogen and phosphorus reduction effect evaluation method, which comprises the following steps: acquiring multispectral image data and a digital surface model, and acquiring actual gradient, altitude and long-term hydrological dynamic data through ground actual measurement, sampling and investigation; extracting core parameters, correcting a digital surface model by utilizing ground actual measurement altitude data, constructing a target coastal zone precise digital elevation model, determining a gradient and dividing a gradient interval, and identifying a target coastal zone type and a planting / slope protection combined structure by combining multispectral image data, ground survey data and the digital surface model; obtaining a nitrogen and phosphorus reduction reference value according to the core parameters, extracting a rainfall intensity correction coefficient and a water level amplitude correction coefficient in combination with hydrological dynamic data, and calculating a final nitrogen and phosphorus reduction proportion; and spatial visualization is carried out, so that efficient, accurate and standardized evaluation of the nitrogen and phosphorus reduction effect of the coastal zone is realized, and a scientific basis is provided for design, optimization and effect inspection of ecological restoration engineering of the coastal zone.

The application relates to a low-altitude signal coverage prediction method and system based on spherical non-uniform sampling, wherein the method comprises the following steps: determining the region complexity of different regions according to a digital surface model graph, constructing a direction sampling weight function according to the region complexity and an antenna directional diagram gain; performing spherical non-uniform sampling on a unit sphere based on the direction sampling weight function to generate a ray direction set matched with the sampling weight; calculating the spherical solid angle corresponding to each sampling direction based on the ray direction set, calculating the transmission power corresponding to each ray according to the spherical solid angle and a base station antenna directional diagram; taking the ray direction set and the transmission power as inputs to perform ray tracking propagation calculation to obtain the received signal power of each sampling point at low altitude, and the low-altitude signal coverage prediction is completed. The problems of how to improve the efficiency and accuracy of low-altitude signal coverage prediction in the related art are solved.

The application provides a post-earthquake building damage state judgment and latitude and longitude inversion method based on oblique images, comprising: obtaining post-earthquake oblique photography images, obtaining a normalized digital surface model (nDSM) based on the oblique photography images; performing multi-threshold segmentation on the normalized digital surface model (nDSM) to preliminarily identify damaged buildings and obtain a damage rate of regional buildings; extracting building multi-side images from the oblique photography images, identifying earthquake damage information, and extracting refined earthquake damage features including cracks and wall peeling; comprehensively judging the damage grade of a target building according to the damage rate of regional buildings and the refined earthquake damage features; and based on the earthquake damage information identified by the earthquake damage information, the geographical latitude and longitude coordinates corresponding to the earthquake damage information are calculated through color spot positioning and coordinate conversion. The application realizes full-process automation from data collection to position output, has high practical value and emergency response capability.

A method of fabricating plasmonic gold prism arrays for digital surface enhanced Raman spectroscopy, a plasmonic gold prism array arranged on a substrate for digital surface enhanced Raman spectroscopy, and a method of using a plasmonic gold prism array that is arranged on a substrate for digital surface enhanced Raman spectroscopy are disclosed. The method of fabricating plasmonic gold prism arrays for digital surface enhanced Raman spectroscopy includes depositing more than one layers of chromium and gold onto a plasmonic substrate comprising a monolayer of polystyrene beads and removing the monolayer of polystyrene beads under sonication in ethanol.

The application belongs to the technical field of remote sensing identification, and relates to a UAV remote sensing extraction method for early missing seedling positions of strip intercropping soybeans. Visible light images are acquired by using a UAV to generate orthographic images and digital surface models, and RGB-Veg multi-channel data is constructed. In combination with an improved SegNeXt model, accurate identification of soybean seedling pixel patches is realized. Through connected domain analysis and centroid extraction, seedling centroid coordinates are obtained to solve the identification problem. Secondly, the seedling centroid point set is clustered by using K-means clustering to distinguish planting strips, and a least square method is used to fit the seeding rows to complete the numbering and sorting of the seedling centroids, construct a structured database, and convert the identification result into point element data with spatial topological relations to provide data support for missing seedling positioning. Finally, according to the seeding plant distance, a missing seedling critical distance is set as a discrimination threshold, and the missing seedling sections are discriminated row by row to further calculate the missing seedling number and extract the three-dimensional coordinates of the positions of the missing seedlings, so that the rapid positioning of the missing seedlings and the timely decision of soybean reseeding can be realized.

This invention discloses a three-dimensional terrain construction method based on multimodal semantic constraints, belonging to the field of three-dimensional terrain modeling technology. This method acquires optical remote sensing images and digital surface models and constructs unified spatial reference data. Guided by multimodal semantic constraints, it performs joint semantic parsing of ground features to generate a set of semantic masks with structural consistency. Furthermore, based on the mapping relationship between semantic categories and surface physical attributes, it performs regional geometric reconstruction of the digital surface model to form three-dimensional terrain entities that conform to the spatial expression characteristics of ground features. Simultaneously, by parsing engineering cost documents and extracting spatially oriented engineering cost data, under spatial and semantic consistency constraints, it associates and maps engineering cost information with three-dimensional terrain entities, ultimately forming a three-dimensional terrain data result that integrates terrain geometric information and engineering economic attributes.