695 results about "Image stitching" patented technology

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention belongs to the technical field of topographic mapping, and discloses a topographic mapping system and method based on an unmanned aerial vehicle, and the method comprises the steps: a data collection module collects map geological data; the route planning module plans an optimal driving route; the data acquisition module acquires topographic images and point cloud data; the environment parameter monitoring module monitors flight environment parameters in real time; the image data analysis module is used for preprocessing and analyzing the image data; the radar data analysis module preprocesses and analyzes the radar data; the feature extraction and matching module performs feature extraction and matching on the image and the point cloud data; the terrain model construction module constructs a terrain three-dimensional model; and the terrain analysis module performs quantitative analysis on the terrain. According to the method, key feature extraction is performed on images and point cloud data, and image splicing and point cloud registration are realized; the terrain model construction module constructs a three-dimensional terrain model according to feature extraction and matching results, and the terrain analysis module obtains accurate terrain information.

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

The invention discloses a tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention relates to the technical field of virtual reality, in particular to an indoor structure reconstruction method based on a panoramic image scene understanding algorithm, and the method comprises the steps: S11, obtaining a plurality of equidistant columnar projection panoramic images of a current indoor scene through a panoramic camera or a panoramic image splicing algorithm; s12, performing semantic segmentation on the collected panoramic image by using SAM, deducing indoor ceiling, floor and wall areas of the panoramic image on the basis of a semantic segmentation result, and generating a multi-channel semantic graph; and S13, inputting the acquired RGB panoramic images and the multi-channel semantic map into a pre-trained panoramic image depth estimation model to obtain a depth map corresponding to each panoramic image. The method is used for automatically generating an indoor structure model, the scheme comprises the core steps of semantic segmentation and layout reasoning, depth estimation and point cloud construction, structure optimization and indoor model reconstruction and the like, and a complete indoor panoramic image scene understanding scheme is formed.

The invention discloses a multi-view underwater three-dimensional point cloud splicing method based on sparse and dense point cloud fusion, and relates to the technical field of structure underwater detection. The method comprises the steps that a binocular camera surface image of an underwater structure is received, the binocular camera comprises a left camera and a right camera, dense point clouds are generated based on stereo matching and triangulation, and the corresponding relation between the dense point clouds and image pixels of the left camera is output; and based on the received left camera image, a sparse point cloud is generated by adopting a motion structure recovery algorithm and utilizing multi-view triangulation, and a corresponding relation between the sparse point cloud and the pixels of the left camera image is output. According to the method, when the point cloud of multi-view reconstruction is spliced, the surface features of the underwater structure can be well restored, feature extraction is not needed, the method has high accuracy and robustness, and high-precision point cloud splicing can be achieved in the underwater complex environment, so that the method has good application prospects in actual underwater structure detection and image splicing tasks.

The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to a crop leaf area index estimation method based on unmanned aerial vehicle image processing. The method comprises the following steps: acquiring a remote sensing image of an unmanned aerial vehicle monitoring area; identifying a topographic relief area according to the unmanned aerial vehicle monitoring area remote sensing image, and performing orthoimage reconstruction on the topographic relief area to generate a topographic relief area orthoimage; identifying a terrain shielding area based on an unmanned aerial vehicle monitoring area remote sensing image; image stitching distortion detection is carried out on the terrain occlusion area to obtain image stitching distortion data; performing spectral interpolation on the gap region based on the image splicing distortion data, and determining a plant growth stage of the gap region after the spectral interpolation; and performing lens distortion detection based on the image splicing distortion data to obtain lens distortion data. The precision and accuracy of crop leaf area index (LAI) estimation are improved based on the unmanned aerial vehicle remote sensing technology.

A method and system for constructing a three-dimensional (3D) aerial survey of a city street scene include obtaining a plurality of video frames from a calibrated multi-camera setup covering a 360-degree view mounted on a moving vehicle. The plurality of video frames is split into a plurality of 3D parts containing a subset of the plurality of video frames and preprocessing the subset of the plurality of video frames of each part of the plurality of parts to obtain a calculated information. Further, constructing, by the processing circuitry, a 3D representation of each part of the plurality of parts based on the calculated information to obtain a plurality of local 3D reconstructed scene intervals. The method includes stitching and filtering, by the processing circuitry, the plurality of local 3D reconstructed scene intervals to construct the 3D city street scene.

The invention relates to an automatic application program control method based on a visual language large model agent, and belongs to the technical field of information. According to the method, a user automatic control task is jointly completed in a cooperative scheduling mode of a plurality of VLM agents, and an agent decision-making method which takes instant strategy planning as a main mode and takes global strategy planning as an auxiliary mode is adopted, so that the defects of the global strategy planning are overcome, and the generalization ability and universality of the method are improved. In order to excavate the potential of the VLM Agent in solving the automatic control problem of the application program, a general rule element extraction mode is adopted to replace a mainstream GUI-Grounding method so as to improve the UI control identification accuracy as much as possible. Besides, according to the method, multi-modal messages generated when the VLM Agent executes the automatic control task are spliced by using an image splicing technology, so that the proportion of multi-round long dialogue type image information in cue words is reduced, the operation speed of the method is improved, and the problem that the shared historical context is too long is relieved.

The invention relates to the field of visual measurement of parts, in particular to a non-contact micropore form and roughness measurement method and system. The method comprises the following steps: carrying out initial contour scanning on a precision part, carrying out part detection base point analysis, and extracting an optimal coordinate base point; performing full-coverage scanning planning and panoramic image splicing based on the optimal coordinate base point to obtain a panoramic fusion image; geometric structure point depth identification is carried out on the panoramic fusion image, three-dimensional point cloud modeling is carried out, and a part CAD digital model is constructed; carrying out micropore shape key size calculation based on a part CAD (Computer Aided Design) digital model, carrying out geometric comparison calculation, and extracting an error value of each measurement point; and performing deviation visual mapping and comprehensive evaluation according to the error value of each measurement point, and outputting a comprehensive evaluation report. According to the invention, the precision part detection efficiency and precision are improved, and the production quality control and later problem traceability of the precision part are enhanced.

The invention provides a capsule endoscope intestinal panoramic image splicing method and device, a computer readable storage medium and computer equipment. The method comprises the following steps: performing feature matching on an original intestinal tract image set acquired by a capsule endoscope to obtain a feature point set of each image of the original intestinal tract image set; performing polar coordinate expansion on each original intestinal image to obtain a rectangular intestinal image and a new feature point set corresponding to the feature point set in the rectangular intestinal image; calculating the displacement difference of each new feature point according to the coordinates of each new feature point of the new feature point sets of the two adjacent frames of rectangular intestinal tract images; cutting according to the displacement difference to obtain a stepping region image between two adjacent frames of original intestinal tract images; performing polar coordinate expansion on the stepping area image to obtain a newly added image segment; and splicing the newly added image segments in sequence to obtain an intestinal panoramic image. According to the method, a more accurate real stepping distance is obtained through calculation, and the smoothness and continuity of the image splicing position can be improved.

The invention discloses an unmanned aerial vehicle image seamless rapid splicing method based on multi-view geometry, and particularly relates to the technical field of unmanned aerial vehicle image splicing, and the method comprises the steps: extracting feature points of video frames through a deep learning model, and carrying out the inter-frame matching, and obtaining a matching point set; based on the matching point set, solving the three-dimensional coordinates of the 3D map points, determining the initial pose of the key frame and initializing the global map, matching the feature points of the current frame with the 3D map points in the global map, and solving the camera pose of the current frame; a new key frame is selected according to the feature coverage rate, a local optimization window is constructed for 3D map points jointly observed by the newly added key frame and the associated key frame, error correction is carried out on key frame poses and 3D map point coordinates in the window through local light beam adjustment optimization, and a global map is updated; and based on the optimized global 3D map point fitting reference surface, projecting the key frame image to the fitting reference surface, generating a panoramic image by adopting a multi-scale pyramid fusion method, and outputting a final image spliced image.

The invention discloses a target recognition method based on multi-view vision and a target recognition model training method, belongs to the technical field of target recognition, and solves the problem that global information is easy to lose due to the fact that a binocular vision method adopted in an existing method mainly depends on target detection after disparity maps and image splicing in the aspect of feature extraction. The method comprises the steps of performing pre-recognition processing on a target scene image based on an improved deep belief network model, responding to an auxiliary adjustment instruction by a multi-view vision acquisition system, fusing a pre-recognition result and a scene compensation image based on a spatial stamp alignment algorithm, and performing scene compensation. The target recognition model extracts global information of a scene fusion set in a parallel mode of combining a bidirectional feature pyramid network architecture with a YOLO algorithm; according to the invention, accurate identification of the target object type and the target object position in the three-dimensional space of the target scene is realized by adopting a cooperative cooperation mode of the improved deep belief network model and the target identification model, and the problems of global information loss and information insufficiency of the scene fusion set during target identification are avoided.

The invention discloses an infrared satellite data processing system suitable for infrared small target detection, and relates to the technical field of remote sensing satellite data processing and infrared image recognition, and the system comprises an image splicing module which is used for arranging L0-level original infrared images according to a shooting time sequence, and identifying overlapped region feature points of adjacent image particles through an SIFT algorithm, eliminating radiation distortion of an optical lens, and generating a seamlessly spliced comprehensive image by adopting a multi-scale fusion technology. According to the infrared satellite data processing system suitable for infrared small target detection, the quality and consistency of infrared satellite images are remarkably improved through a standardized data processing flow, the identifiability of target signals is optimized from a data source, the false alarm rate under the conditions of a complex background and a low signal-to-noise ratio is effectively reduced, and the detection accuracy is improved. And high-quality input is provided for a subsequent detection algorithm, so that the accuracy and reliability of infrared small target detection are improved.

The invention provides a photovoltaic module damage intelligent detection method and system based on an unmanned aerial vehicle image, and relates to the technical field of unmanned aerial vehicle image.An unmanned aerial vehicle collects a multi-angle high-altitude image of a photovoltaic module array according to a predetermined path and records geographic position information; image splicing and overlap region correction are carried out by using the information to form a high-resolution panoramic image; in combination with a pre-constructed three-dimensional topological structure model, identifying and marking photovoltaic modules in the panorama, and generating a module distribution diagram; by analyzing the actual spectral response characteristics of each component, comparing standard data in a material science database, and screening suspected damage areas; applying an adaptive threshold algorithm and morphological operation to the areas to generate a feature map; and integrating real-time environmental factors and historical performance changes, extracting target features influencing the performance reduction of the photovoltaic module, predicting a damage state, and finally generating a detection report. According to the invention, the detection efficiency of the photovoltaic module is improved.

The invention discloses a microscopic image scanning and parallel efficient splicing method based on a spanning tree, which considers the characteristic that the scanning visual angle of a microscope is distortionless and the characteristic that the corresponding relation between the physical scale and the pixel scale of a microscope platform is unchanged, and has the advantages of an image matching algorithm based on a template. Automatically dividing an overlapping region range for a template matching algorithm, fully considering the matching result credibility of the pictures on the surrounding adjacent sides, and calculating a global splicing point location by using a spanning tree algorithm; picture splicing types are divided through rows and columns, the problem of memory access conflicts is avoided, the computing power improvement brought by multiple threads is fully utilized, and the picture splicing efficiency is improved. According to the method, adjacent image matching, high-precision matching of global splicing point locations based on the spanning tree and a multi-thread high-speed image splicing technology are realized.

The present invention relates to a compaction monitoring device and method that integrates 3D scanning and image perception. The device comprises a sensing component comprised of a 3D road surface texture scanning module, a camera, a vibration damping module, and a locator; a data analysis component, which performs image positioning, image stitching, time synchronization, and calculation of a compaction index based on texture features; and a control strategy component, which controls the number of compaction attempts based on the surface distribution of the compaction index. Compared to existing technologies, this invention introduces a new information source and evaluation basis for road compaction, enabling dynamic, real-time monitoring and feedback control of compaction levels, improving the quality of road construction projects and enabling compaction monitoring based on 3D scanning and image perception.

The invention discloses a global scanning image splicing and defect positioning system for an ultra-large-diameter quartz crucible, and relates to the technical field of quartz crucibles, and the system comprises a scanning control module which constructs a fusion model through multiple sensors, evaluates a risk coefficient, obtains the fusion model, dynamically plans a multi-arm path of a robot based on reinforcement learning, and sends the multi-arm path of the robot; adaptively adjusting the acquisition frequency of the corresponding area based on the risk coefficient, and outputting multi-modal data through a multi-round feedback iteration optimization acquisition process; the image splicing module is used for acquiring multi-modal data for local splicing, and generating a panoramic image in combination with a multi-arm path of the robot; the defect positioning module is used for constructing a quartz crucible model through the panoramic image, detecting defects in a layered manner by utilizing a YOLOv5 model, tracking the defects through extended state Kalman filtering, quantifying the defect volume and the hazard level by combining point cloud semantic segmentation, and performing 3D positioning and evaluation on the defects; the output data is ensured to always meet the subsequent image splicing and defect positioning requirements, and the system stability is guaranteed.

The invention discloses a high-precision detection method for a fault electrode of an electrolytic cell, and the method comprises the following steps: S1, carrying out the equipment layout, carrying out the infrared thermal imaging image collection, employing a high-resolution and large-field-angle lens collection device, improving the infrared image effect, improving the detection range, and installing uniformly-distributed current measurement devices, such as a Hall effect sensor, in the electrolytic cell; s2, image acquisition equipment is installed, the image acquisition equipment is installed through a visiting support and related assemblies, and meanwhile, the system adopts an automatic cruise and visiting mode to observe and troubleshoot field fault electrodes. According to the electrolytic cell fault electrode high-precision detection method and the positioning system, through infrared thermal imaging image acquisition, image splicing, deep learning algorithm convolutional neural network identification of a fault electrode plate and calling of a current matrix for analysis, through comparison of the two, a fault is determined through dual-mode fault detection; misjudgment is avoided; and the accuracy of fault pole plate positioning is improved.

The invention discloses a vertical rail scanning remote sensing satellite image splicing method and system, and belongs to the technical field of remote sensing image processing. The method is used for solving many challenge problems, including inter-strip positioning deviation, image distortion, scale difference and discontinuous brightness color, faced by an existing remote sensing image splicing method in fusion processing of vertical rail scanning multi-strip images. According to the method, seamless fusion is completed through feature extraction and matching, RANSAC matching optimization, optimal splicing line generation, seam area feathering processing and seamless image output, robust matching and homography transformation are obtained by extracting SIFT features and combining an RANSAC algorithm, and the image registration and splicing precision is remarkably improved; the splicing line is generated by adopting dynamic planning, and feathering processing is performed on the splicing edge, so that seam artifacts and brightness inconsistency caused by multi-strip splicing are effectively reduced, and the continuity and visual consistency of the spliced image are improved. The method is suitable for large-range remote sensing data fusion and application.

In various examples, color statistic(s) from ground projections are used to harmonize color between reference and target frames representing an environment. The reference and target frames may be projected onto a representation of the ground (e.g., a ground plane) of the environment, an overlapping region between the projections may be identified, and the portion of each projection that lands in the overlapping region may be taken as a corresponding ground projection. Color statistics (e.g., mean, variance, standard deviation, kurtosis, skew, correlation(s) between color channels) may be computed from the ground projections (or a portion thereof, such as a majority cluster) and used to modify the colors of the target frame to have updated color statistics that match those from the ground projection of the reference frame, thereby harmonizing color across the reference and target frames.

The invention relates to a photovoltaic land slope stability partition evaluation method, which comprises the following steps that: an unmanned aerial vehicle flies in an oblique photography manner according to a path planning algorithm, and acquires an image and point cloud data of a slope region; correcting the collected images, including distortion removal, noise processing, image splicing, denoising, filtering and point cloud splicing of the point cloud data; creating a real three-dimensional terrain model by combining the texture information of the image and the height data of the point cloud, and extracting terrain height information from the point cloud data to generate a digital elevation model; performing feature extraction on the three-dimensional terrain model, and analyzing a stability index of the slope; establishing a slope stability evaluation model and performing zoning evaluation, performing stability zoning evaluation according to the feature data and the clustering result, and optimizing a zoning standard; and visualizing a slope stability partition result, generating a stability partition map and a report, establishing a dynamic monitoring system, and regularly using the unmanned aerial vehicle to repeatedly fly to obtain updated slope data.

The invention relates to a precision agriculture monitoring system and method based on multispectral imaging. The system and method are applied to real-time monitoring of crop physiological parameters and variable fertilization decision making. The system comprises an unmanned aerial vehicle imaging module, an edge computing unit and a cloud analysis server. The unmanned aerial vehicle module is provided with a multispectral filter wheel, a three-axis holder and an RTK positioning device and is used for acquiring a high-resolution crop image; the edge calculation unit integrates a radiation correction module, an image splicing module and a canopy segmentation module to realize on-site preprocessing; the cloud server runs a deep learning model and a feature fusion mechanism, outputs estimation of parameters such as nitrogen, chlorophyll and moisture, and generates a high-resolution fertilization prescription map. In the aspect of the method, dynamic monitoring of the nitrogen content of crops is realized through route planning, data synchronization, radiation normalization, multi-source feature fusion and time sequence prediction. The system supports online updating and ground verification of the model, has high precision, low delay and large-area operation capability, and is suitable for intelligent agriculture and precise fertilization scenes.

The invention discloses a cultural relic exhibition hall AI intelligent shooting system based on binocular vision, and relates to the technical field of cultural relic digitization and interaction, and the system comprises a binocular vision image collection module which is used for shooting and storing a multi-view high-definition image of a cultural relic; the AI image splicing fusion module preprocesses the image and generates a panorama; the cultural relic three-dimensional reconstruction module constructs a three-dimensional model with fine textures; real-time fusion of the intelligent shooting module fusion model and a visitor image is carried out to generate an interactive picture; the interactive output and management module provides related functions and manages data; by integrating binocular vision image acquisition, AI image splicing and fusion, cultural relic three-dimensional reconstruction and real-time fusion intelligent shooting technology modules, intelligent upgrading of cultural relic display and visitor interaction is achieved, the system adopts a binocular camera shooting mechanism triggered by multiple strategies, timing and induction triggering modes are combined, and the intelligent upgrading of cultural relic display and visitor interaction is achieved. The method can flexibly adapt to the demands of different exhibition scenes, guarantees the high efficiency of cultural relic image collection, and avoids the waste of resources.

The invention is applicable to the technical field of industrial image processing, and provides a coating surface defect grade image recognition and evaluation method, which is based on deep learning and image splicing technologies, comprises the following steps of: shooting substrate images in blocks, splicing the substrate images into a high-precision complete image, extracting an ROI (Region of Interest), and inputting the ROI into an improved semantic segmentation model; and outputting a defect mask and calculating an area ratio, and performing automatic defect grade evaluation by combining GB / T 1766-2008 and ISO 4628-3: 2016 standards. According to the method, the problems of low manual detection efficiency, high subjectivity and inaccurate small-area defect rating are solved, the detection speed is increased to 3-5 seconds per sample, the rating result is objective and reliable, and the method is suitable for efficient detection and quantitative analysis of coating defects such as rusting, peeling and bubbling after the salt spray test.

The invention relates to the technical field of image processing, in particular to a cultivated land monitoring method and system based on an unmanned aerial vehicle. The method comprises the following steps: obtaining adjacent key image frames of unmanned aerial vehicle video data, and splicing the adjacent key image frames to obtain a panoramic image; performing panoramic segmentation on the panoramic image and obtaining masks reflecting different types of cultivated land through an SEEM model; and extracting the contour of the mask, converting the extracted contour into coordinates in a geographic coordinate system, and mapping each coordinate point with the land category to which the coordinate point belongs. According to the application, farmland monitoring can be carried out by using the aerial image of the unmanned aerial vehicle, the farmland is monitored in real time, meanwhile, image processing is carried out by using the panoramic image splicing technology and the SEEM model, a wider view can be obtained, the limitation of a single image is eliminated, and refined management of the farmland is realized.

The invention relates to the field of ecological environment monitoring, and discloses a vegetation coverage spatio-temporal change analysis method, which comprises the following steps of: firstly, preprocessing remote sensing image data of a research area, including radiometric calibration, atmospheric correction and image splicing and cutting; then calculating a vegetation index value based on the normalized vegetation index, and extracting a vegetation coverage through a pixel bipartite model; and performing difference calculation on the vegetation coverage data in different periods, analyzing spatial and temporal change characteristics of vegetation coverage, and generating a vegetation coverage grading map and a change distribution map. According to the method, the spatial distribution and dynamic change trend of vegetation coverage can be accurately reflected, the defect that a traditional vegetation monitoring method is sensitive to soil background and atmosphere interference is overcome, the method has the advantages of being high in precision, wide in applicability and high in dynamic monitoring capacity, and a scientific basis is provided for formulation and effect evaluation of regional ecological protection measures.

The invention discloses a forest stand tree species identification method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining data which comprises forest land remote sensing image data and ground data; carrying out preprocessing on the obtained data; a dual-path CBAM-UNet network model is constructed; inputting the preprocessed data into the double-path CBAM-UNet network model to carry out tree species classification prediction and obtain a tree species classification chart; performing image splicing on the tree species classification map to obtain a tree species classification grid map; the system comprises a data acquisition module, a data preprocessing module, a prediction module and an image processing module. According to the forest stand tree species identification method and system based on multi-source remote sensing data fusion provided by the invention, multi-scale feature capture and feature fusion of multi-source heterogeneous data can be realized through the constructed dual-path CBAM-UNet network model, high-precision identification can be performed on forest stand tree species under complex links, and high-quality data can be provided for forest management.