714 results about "Scale space" patented technology

The invention provides a multi-mode large model-based precision metal part defect detection method and system. The method comprises the following steps of: acquiring laser scattering image data of a precision metal part in an electromagnetic shielding environment; performing multi-scale spatial analysis on the laser scattering image data to extract a multi-modal image feature set containing a crystal boundary topological structure of the surface of the precision metal part and scattering spot distribution gradient features of the subsurface; converting process parameters, corresponding to the precision metal part, in an existing metal heat treatment process database into heat historical feature vectors, wherein the heat historical feature vectors comprise temperature gradient distribution features and time sequence phase change features; performing fusion processing on the thermal historical feature vector and the multi-modal image feature set through a multi-modal large model to obtain a fusion processing result so as to generate defect detection data; according to the invention, the detection rate and the detection efficiency of micron-sized defects in a complex electromagnetic environment are improved.

The invention relates to the technical field of water conservancy project detection, and discloses a water conservancy project safety detection early warning method based on artificial intelligence. The method comprises the following steps: acquiring multi-modal monitoring data of a key part through a distributed sensor network, and extracting a dynamic feature sequence in a preset time period through space-time alignment and noise filtering; inputting the image into a deep neural network fused with an attention mechanism, constructing a multi-scale space-time correlation map through hierarchical feature learning, and generating a high-dimensional representation of an engineering structure state; historical accident case data is used as a supervision signal, a hybrid expert model is used for performing multi-task training on high-dimensional representation, and the contribution weight of each monitoring index to the safety risk is obtained; combining real-time environment parameters and structural response characteristics to construct a dynamic threshold adjustment model, adaptively updating an early warning threshold according to a risk probability, and screening out key risk factors of which the contribution weights are greater than the updated threshold; and on the basis of spatial and temporal distribution characteristics, through graph neural network node association reasoning, multi-source early warning information is fused to generate a graded early warning result.

The invention relates to the technical field of airport safety monitoring, and discloses an airport video data real-time analysis system. The system comprises a video stream spatial-temporal feature modeling module, a behavior trajectory map construction module, an abnormal region association analysis module, a risk level semantic judgment module and a situation structure visualization module. According to the method, multi-scale spatial-temporal feature analysis is carried out on an airport monitoring video stream, a multi-dimensional behavior trajectory map is established, abnormal behavior region association is analyzed, risk level semantics are judged, and finally an airport global risk situation thermodynamic distribution map is generated. According to the system, the whole process processing from video data acquisition to risk situation visualization is realized, the abnormal behavior area can be accurately identified, the risk level and category are clear, comprehensive and visual situation information is provided for airport safety management, and the intelligent level of airport safety management is improved.

The invention relates to the technical field of face recognition, in particular to a face recognition method and system for a dynamic environment, and the method comprises the steps: collecting a face video stream through a multispectral imaging device, and carrying out the preprocessing of dynamic noise reduction, distortion correction and the like; constructing a multi-scale space-time fusion feature extraction network to extract dynamic space-time features and fuse cross-modal features; establishing an environment disturbance simulation generation model to generate a virtual sample, and performing domain adaptive alignment; designing an online incremental feature updating mechanism to optimize parameters of the feature encoder; deploying a heterogeneous graph neural network to carry out multi-modal decision fusion; and a hierarchical verification architecture is adopted to complete identity recognition. The system comprises a data acquisition and preprocessing module, a multi-scale space-time fusion feature extraction module and the like. According to the method, the problem of face recognition in a dynamic environment is effectively solved, the recognition accuracy, robustness, real-time performance and reliability can be remarkably improved in the scenes of complex illumination, posture expression change, shielding, background noise and the like, and the method has a wide application prospect.

The invention relates to the technical field of surveying and mapping information processing, and discloses a surveying and mapping information intelligent analysis and visualization system based on the Internet of Things. According to the system, a building space data acquisition terminal acquires geometric feature point cloud and environmental parameters of the surface of a building structure in real time through distributed Internet of Things nodes; the multi-source heterogeneous data fusion engine performs space-time alignment processing on the geometric feature point cloud to generate a building total-factor topological skeleton with an absolute coordinate system; the three-dimensional topological relation reconstruction module is used for extracting a spatial constraint relation of building components according to the topological skeleton and establishing a hierarchical building information model capable of being edited in a parameterized manner; and the dynamic visual rendering platform is used for generating a multi-scale spatial index based on the hierarchical building information model, and driving real-time light and shadow effects and interactive sectioning analysis. According to the system, full-process automatic processing and dynamic visualization of building surveying and mapping data are realized.

The invention relates to the technical field of machine vision, and discloses a surveying and mapping geographic information analysis method and system based on machine vision and a medium. The method comprises the following steps: preprocessing multi-source remote sensing data to obtain a standardized surveying and mapping data set, and extracting ground feature edge and texture information to obtain a multi-scale spatial feature and a semantic segmentation result; carrying out multi-source data integration to obtain a unified geographic element representation diagram, and carrying out geographic element identification on the unified geographic element representation diagram to obtain a surveying and mapping geographic element set; performing Bayesian inference of spatial uncertainty quantization and parameter uncertainty quantization on the surveying and mapping geographic element set to obtain a comprehensive uncertainty evaluation result; and constructing vectorized geographic information based on the surveying and mapping geographic element set and the comprehensive uncertainty evaluation result. According to the method and the device, the edge information of the geographic elements is effectively reserved, the identification precision of the geographic elements is improved, the geographic information expression is more structured, and automatic updating is realized.

The invention discloses a scene text recognition method based on optimized multi-modal vision and language processing, which comprises the following steps of: firstly, normalizing image data; and then inputting the preprocessed data into the optimized visual model. The visual model extracts multi-scale space and semantic features through a convolution-Transform hybrid neural network, and enhances the feature expression ability by using a multi-scale attention mechanism; the language model corrects character probability vectors output by the visual model, and learnable position codes are introduced to optimize representation of the features. Through designing a bidirectional multi-modal interaction module, visual and language features are fused, and a self-adaptive fusion mechanism is used to generate high-quality multi-modal joint feature representation. In the application stage, the optimized model is deployed through an efficient reasoning framework, and the speed and accuracy of scene text recognition are remarkably improved.

The invention discloses a fine-grained zero-sample medical image classification method based on cross-modal feature alignment, and the method comprises the steps: 1, carrying out the partitioning of a full-section pathological image, and extracting the features of a local image block; 2, a cross-modal alignment module is used for designing a local window attention mechanism to enhance space interaction between image blocks; the semantic enhancement module is used for constructing a pathology prompt template based on a large language model to generate fine-grained category description, and expanding the distance between categories in a semantic space; and 4, performing weighted fusion on the image block features through coordinate sensing, and generating final slice-level classification prediction. According to the invention, through multi-scale space interaction of the cross-modal image block alignment module and semantic enhancement of the semantic refinement module based on the visual language model, the classification precision of the fine-grained medical image is significantly improved, and the limitation of the existing method on feature alignment and semantic differentiation is effectively solved; and an efficient solution is provided for zero-sample medical image classification.

The invention discloses a medical image classification method and system based on multi-scale spatial state modeling, and the method comprises the steps: firstly dividing an input medical image into a plurality of non-overlapping image blocks, and mapping the non-overlapping image blocks to a feature space through a learnable linear projection layer to obtain an initial feature map; then, multiple layers of stacked MS-SMamba blocks are used for carrying out layer-by-layer feature extraction, each MS-SMamba block comprises a main branch, an auxiliary branch, a dynamic gating fusion network, a residual error connection unit and a feedforward network, and long-range dependency relation capture and multi-scale feature fusion are achieved; and finally, processing the last-layer output feature map through a global feature aggregation and classification module, generating a global feature vector, and outputting a classification result. According to the method, the capturing capability of complex pathological features in the medical image is improved, the calculation efficiency and clinical applicability are improved, and the method is suitable for scenes such as disease screening and auxiliary decision making in medical image diagnosis.

The invention relates to the technical field of deep learning image recognition, in particular to an unmanned aerial vehicle small target detection method based on deep learning, and the method comprises the steps: embedding a parallel block perception attention module in a C2f module of YOLOv8; a large separable nuclear attention mechanism is fused in the SPPF module of the YOLOv8; the original nearest neighbor interpolation is replaced by CARAFE dynamic up-sampling; the Soft-NMS is adopted to replace the NMS; reconstructing a Neck part of the backbone network; a dynamic target detection head is combined with a scale sensing module, a space sensing module and a task sensing module; a WFS-IoU loss function fusing Wise-IoU, Focaler-IoU and Shape-IoU is designed, aiming at the core bottlenecks of feature loss, poor scale adaptability, dense target processing defects and the like in small target detection of the unmanned aerial vehicle, the balance between precision and efficiency is realized through multi-module collaborative optimization and lightweight design, and the detection accuracy is improved. And an efficient and reliable detection solution is provided for aerial photography application of the unmanned aerial vehicle.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

The invention relates to a hyperspectral image classification method and device based on a state space model. The hyperspectral image classification method based on the state space model comprises the following steps: sequentially carrying out feature extraction and serialization processing on hyperspectral image data to obtain a shallow feature projection vector; performing global-local feature extraction on the shallow feature projection vector by adopting a neural network based on a state space model to obtain a fused feature projection vector; and carrying out pixel-by-pixel classification and dimension rearrangement on the hyperspectral image data in sequence to generate a classification result of the hyperspectral image data. According to the hyperspectral image classification method based on the state space model, long-range dependence modeling is achieved through the neural network based on the state space model with linear complexity, the calculation complexity is effectively reduced, and through feature fusion and residual error connection, the classification accuracy of the hyperspectral image is improved. And the perception capability of the neural network on different scale space-spectrum structures in the hyperspectral image is effectively enhanced.

The invention discloses a complex scene-oriented target identification method, system and device and a medium, and relates to the technical field of computer vision. The method comprises the following steps: inputting a to-be-detected image into a target recognition model for processing to obtain a target recognition result; the target identification model is constructed based on a dual-channel coding network and a weighted optimization loss function; the dual-channel coding network comprises a MobileNet channel and a wavelet channel; wherein the MobileNet channel is used for extracting multi-scale spatial features and is composed of a layered encoder and a multi-feature prediction decoder which are connected in sequence; the wavelet path is used for extracting frequency domain features and is composed of a global semantic wavelet coding module and a local wavelet fusion decoding module which are connected in sequence; and the multi-feature prediction decoder is also respectively connected with the global semantic wavelet coding module and the local wavelet fusion decoding module. According to the invention, the target identification precision and accuracy for complex scenes can be improved.

The invention provides a dense overlapping target detection method based on a wavelet enhancement sparse hybrid expert model. The method comprises the following steps: firstly, extracting multi-layer features through a backbone network to capture multi-scale spatial representation; secondly, discrete wavelet transform is introduced to each level of features, spatial features are decomposed into a frequency domain, collaborative modeling of frequency domain and spatial domain features is realized, the reservation capability of detail and texture information is improved, a lightweight dynamic hypergraph aggregation module is introduced into the deepest layer of features, a hyperedge structure is adaptively learned, and the feature fusion is realized; modeling a high-order incidence relation in a local area in an explicit manner; and thirdly, in the decoding process, candidate queries are screened and reweighted through an IoU perception query selection mechanism, and a dynamic routing mechanism of sparse hybrid experts is introduced, so that query self-adaptive specialized representation learning is realized, and the target detection precision and reliability in a complex scene are effectively improved.

The invention discloses a method, a system, equipment and a medium for analyzing project progress based on a BIM model and an AI video, and relates to the technical field of constructional engineering management, and the method comprises the steps of collecting regional image data, and executing semantic segmentation and environment modeling. And constructing a multi-scale spatial feature network and a dynamic skeleton modeling engine, inputting the three-dimensional environment diagram, the structure recognizable region diagram and the obstacle probability distribution diagram into the multi-scale spatial feature network and the dynamic skeleton modeling engine, and outputting execution structure recognition and defect anomaly labeling. And triggering an engineering response mechanism based on the risk event scoring function and remotely and dynamically adjusting the construction path. According to the method, through a mode of combining semantic segmentation and three-dimensional environment modeling, high-precision structure identification of the construction site image is realized, and the problems of unclear structure identification and fuzzy component boundary in traditional video monitoring are effectively solved.

The invention provides a digital twin three-dimensional model construction method based on a generative large model, and the method comprises the steps: obtaining multi-source monitoring data of a power distribution network, and processing the multi-source monitoring data into a training data set; the method comprises the following steps: mapping multi-source monitoring data into a multi-scale tensor subspace through tensor wavelet structured transformation, adaptively extracting spatial features through a learnable wavelet kernel, and keeping the structural continuity of a physical field in combination with a geometric prior regular term; constructing and training a generative adversarial network through a training data set; inputting and analyzing the physical parameter vector of the target scene, and if the topological similarity score is lower than a preset threshold value, adjusting noise vector regeneration; and if yes, outputting a three-dimensional model tensor and importing the three-dimensional model tensor into a digital twin platform, and driving real-time physical field visualization. According to the method, characteristics of a multi-scale space structure and a nonlinear physical field can be reserved, the physical rationality and generalization ability of the generated model are remarkably improved, and depth identification of topological attributes (such as hole connectivity and surface defects) and local geometric defects of the three-dimensional model is realized.

The invention discloses a regional highway road network habitat quality evaluation method and system, and aims to construct a habitat feature library reflecting habitat spatial features and a biological utilization mode by integrating remote sensing image data and biological activity trajectory data. Identifying an ecological fault zone caused by road network cutting by adopting a space fracture analysis technology, quantifying the barrier strength of the road and determining an influence range; analyzing and extracting a key migration gallery by using a species migration network, and evaluating a landscape connectivity condition; a restoring force loss area is identified through ecological restoring force evaluation, and the habitat degradation dynamic state is revealed in combination with time sequence analysis; establishing a multi-scale space evaluation system, and fusing the quality grading sequence and gallery optimization information to generate a habitat quality evaluation matrix; and finally, a complete habitat quality grading scheme is formed through spatial interpolation and natural breakpoint grading, comprehensive evaluation of the habitat quality of the regional highway network is realized, and a scientific basis is provided for ecological protection planning and road construction decision making.

Some embodiments of a method may include: obtaining a reference 3D Gaussian frame, a camera position C, and a time t; extracting a multi-scale feature for each 3D Gaussian of one or more 3D Gaussians using a neural network block, wherein the multi-scale feature represents multi-scale spatial information about a dynamic object or scene; predicting 3D motion based on the multi-scale features and the time t; predicting a 3D Gaussian frame for time t by manipulating the one or more 3D Gaussians in a spatial domain based on the predicted 3D motion; and outputting the 3D Gaussian frame for time t.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention discloses a remote sensing image semantic segmentation method based on geometric perception diffusion guidance, and is applied to the technical field of remote sensing image semantic segmentation. Comprising a training stage and a testing stage, in the training stage, original remote sensing images, nDSM corresponding to each original remote sensing image and real semantic segmentation images are selected to form a training sample set, and a segmentation everything model based on geometric perception diffusion guidance is constructed and trained; comprising an enhanced visual converter encoder, a diffusion prompt module, a segmented everything image prompt encoder, a segmented everything image mask decoder and a prompt level supervision strategy. In the test stage, various channel components of a to-be-detected remote sensing image are input into the trained model, and the model network outputs a remote sensing image semantic segmentation prediction map corresponding to an original remote sensing image. According to the method, multi-modal remote sensing data can be effectively fused, a multi-scale space structure is captured, and full-automatic semantic segmentation is realized, so that the segmentation efficiency and accuracy are remarkably improved.

The invention belongs to the field of artificial intelligence and electroencephalogram emotion recognition, and provides a double-branch electroencephalogram emotion recognition method and system based on brain region topology and time-space, and the method comprises the steps: preprocessing a to-be-recognized electroencephalogram signal to obtain a plurality of electroencephalogram fragments, and extracting a difference entropy sequence of each electroencephalogram fragment and a Spearman correlation coefficient matrix between channels; based on the Spearman correlation coefficient matrix, utilizing a bridging dynamic graph attention network module to extract topological features of a brain region; processing the differential entropy sequence by using a multi-scale space-time mixed attention module to obtain multi-scale space-time features; carrying out residual mutual cross attention fusion on the topological features of the brain region and the multi-scale spatial-temporal features to obtain fusion features; and performing classification based on the fusion features, and determining an emotion recognition result corresponding to the electroencephalogram signal. According to the method, the accuracy and robustness of emotion recognition are improved by utilizing the spatial topology characteristics and the multi-topology time dynamic characteristics of the electroencephalogram signals, and the defects of modeling spatial dependence and time dynamic are overcome.

The invention provides a low-illumination perception method and system based on space-frequency fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a normal light image and a low-illumination image corresponding to the normal light image; performing feature extraction on the normal light image and the low-illumination image through an encoder to obtain multi-scale spatial features; decomposing the multi-scale spatial features through stationary wavelet transform to generate a low-frequency feature component and a high-frequency feature component; performing optimization processing on the low-frequency characteristic component and the high-frequency characteristic component based on a parameter learnable frequency domain adaptive filtering mechanism; fusing the optimized low-frequency feature component and the optimized high-frequency feature component by introducing a cross attention learning mechanism to obtain a frequency domain feature; performing adaptive complementary fusion on the frequency domain features and the multi-scale spatial features to generate fusion features; step-by-step up-sampling is carried out on the fusion features through a decoder; and outputting a segmentation result of the low-illumination image through a sensing head according to the fusion features after up-sampling.

The invention discloses an infrared small target detection method based on a multi-scale rotation deformable attention module. The infrared small target detection method comprises the following steps: step 1, collecting an infrared image; 2, extracting and enhancing high-frequency feature representation of the original infrared image; 3, performing deep feature extraction on the high-frequency feature representation, constructing multi-scale feature representation by adopting a feature pyramid network, and performing feature enhancement to obtain multi-scale enhanced features with rich semantic information and spatial details; 4, a multi-scale and multi-direction comprehensive module is adopted, the local space relation between multi-scale enhanced features is extracted, feature representation of the rotating target is enhanced, and self-adaptive positioning and recognition of the target in different directions and sizes are achieved; and 5, dynamically adjusting the target feature weight, obtaining features fusing multi-scale, space and task adaptive information, outputting a target category and a rotating frame parameter by adopting a detection head, and performing training optimization of the detection head. According to the invention, the detection precision of the infrared rotating target is improved.

The invention relates to an event-based lip reading method and device, equipment and a storage medium. The method comprises the following steps: collecting an original event stream of a lip sequence image through an event camera; therefore, the brightness change of each pixel can be asynchronously recorded with microsecond-level time resolution, and ultra-low delay, high dynamic range and sparse data representation are realized. Converting the original event stream into a frame-shaped event tensor based on a voxel representation method to obtain a voxelized event body; performing spatial feature extraction on the voxelized event body through a front-end network in an event-based lip reading model to obtain multi-scale spatial features; performing time-dependent modeling on the multi-scale spatial features through a rear-end sequence model in an event-based lip reading model to obtain a sequence code; therefore, space and time features can be fused, and the accuracy and stability of sequence coding are improved. And determining the lip reading recognition content according to the sequence code. Therefore, stable recognition of lip movement can be realized, and lip reading accuracy is improved.

The invention discloses a three-dimensional blood vessel image segmentation method and system. Belongs to the technical field of medical image processing and particularly relates to the technical field of three-dimensional blood vessel image segmentation. The method solves the following problems existing in a blood vessel segmentation task in an existing method: small blood vessel features are difficult to accurately extract in a CTA image which is low in contrast and contains noise and artifacts; global context modeling is difficult to consider and local correlation is difficult to guarantee, so that long-distance dependent modeling is insufficient or a local structure is fractured; limited by a fixed geometrical shape of a traditional convolution kernel, the traditional convolution kernel is difficult to adapt to deformation characteristics of a complex topological structure of a blood vessel, resulting in discontinuous segmentation or fuzzy boundary of a branch region. Channel dynamic grouping and energy-driven attention generation are achieved through a grouping self-adaptive attention module, and self-adaptive modeling of a blood vessel complex branch structure and a geometrical shape is achieved through a multi-scale space structure aggregation module in combination with a strip-shaped deformable convolution and cross-scale guiding mechanism.

The invention discloses a belt tearing detection model training method and detection method based on space-time sample enhancement, and the training method comprises the steps: collecting an original image sequence of a material conveying belt, and marking the original image sequence to obtain an original mask sequence; performing time sequence sample amplification of random cloning on the time dimension on each original image sequence and the original mask sequence, and further performing scene batch amplification for enhancing sample richness; and performing iterative optimization on pre-constructed belt tearing detection network parameters by using the data subjected to time and space enhancement: capturing time sequence dependence between images, complementing spatial features of key frames with time sequence dependence features, and gradually fusing complementary spatial and temporal feature information with multi-scale spatial features of the key frames to obtain a final feature map. And carrying out region segmentation and error loss calculation on the tearing target, gradually iteratively optimizing a target segmentation result, and finally obtaining a model with good training parameters.

The invention discloses a multi-label arrhythmia detection method based on multi-scale space-time dynamic graph convolution, and the method comprises the steps: capturing the space-time feature information of an ECG signal through series gating time convolution and dynamic graph convolution; a feature fusion module is provided, the single-scale representation capability is enhanced through statistical feature assisted global-local feature fusion, redundant information is successfully removed through orthogonal gating multi-scale fusion, and a gating mechanism dynamically adjusts the importance of each scale feature in the feature screening process, so that the accuracy of the feature screening is improved. The sending end and the receiving end are added to further filter information, so that the risks of multi-scale feature overfitting and information loss are effectively avoided, the robustness and accuracy of the model are improved, and the situation that the most valuable information is not fully reserved during multi-scale spatial-temporal feature fusion due to the fact that redundant features are difficult to effectively distinguish in a traditional method is avoided. The method not only improves the accuracy and stability of the model, but also has high practicability, and can effectively support automatic diagnosis of arrhythmia.

The invention discloses a data container storage and management method based on a multi-scale space subdivision grid, and the method comprises the steps: constructing a multi-scale space subdivision grid coding system covering the earth surface and the height dimension based on an earth space subdivision theory, and endowing each grid with a unique code to form a unified space-time framework; multi-source data of images, vectors, non-space and the like are collected and transmitted to a processing center, and after consistency detection and normalization processing are carried out, unified grid codes are generated for various kinds of data based on the coding system, and mapping is established; and finally, storing the coded data into a database, updating an index, realizing dynamic scheduling, logic aggregation and visual display of multi-source data by means of unified coding, realizing efficient management and control and cross-domain collaboration of massive heterogeneous data, and improving data storage consistency and retrieval and display efficiency. According to the method, the problems of fragmentation of spatial big data, slow retrieval response, difficulty in cross-domain fusion and the like are effectively solved, and the management efficiency and the sharing depth of the spatial data are remarkably improved.

The invention discloses a hyperspectral image restoration method, which belongs to the technical field of hyperspectral imaging, and comprises the following steps: inputting a single hyperspectral image to be restored into a hyperspectral image restoration network for denoising / super-resolution reconstruction, the features are respectively input into a multi-scale space-spectrum fusion module to obtain branch features, local multi-scale space-spectrum features after dimension raising are obtained through splicing and dimension raising, and then the local multi-scale space-spectrum features are input into a self-adaptive space feature aggregation group to obtain global space-spectrum features; and after the noisy hyperspectral image passes through a convolutional layer, fusing the noisy hyperspectral image with the global space-spectral features to obtain a final de-noised image. The extended denoising method can obtain a super-resolution reconstruction method, and the method comprises the steps: carrying out the up-sampling after the global space-spectrum features are obtained; a low-resolution hyperspectral image to be restored is up-sampled before passing through a convolutional layer. According to the invention, the problems of noise introduction and spatial resolution reduction in the hyperspectral imaging process in the prior art are solved.

The invention discloses a GIS-based traffic project land expropriation and demolition digital management system and method, and particularly relates to the technical field of geographic information intelligent monitoring. NDVI vegetation features of satellite images and a building contour feature map of unmanned aerial vehicle data are extracted respectively, unified geographic projection conversion is carried out in combination with a sensor coordinate flow, and the data of the NDVI vegetation features and the building contour feature map are acquired; setting a cross-modal feature alignment mechanism driven by an adversarial network, extracting multi-scale space correlation features through wavelet transform, dynamically generating a space-time consistency compensation coefficient in combination with a sensor displacement vector, and realizing centimeter-level precision multi-source data adaptive fusion by using a dynamic weight fusion model; the spatial distance between a boundary post and a demolition red line is calculated in real time through a ray tracing algorithm, a dynamic safety threshold is generated in combination with a historical dispute rate, the defect of delay of traditional static threshold early warning is broken through, and the bottleneck problems of precision and timeliness in land acquisition and demolition management are solved by constructing multi-source geographic data fusion and dynamic early warning.