21196results about "Scene recognition" patented technology

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The present invention relates to the field of vehicle data processing, and in particular to an intelligent decision support method and system for vehicle-mounted sensor data fusion. The method comprises the following steps: obtaining vehicle multi-source operating status monitoring data based on vehicle-mounted sensors; performing vehicle status heterogeneous fusion and operating status multi-period change analysis on the vehicle multi-source operating status monitoring data, thereby obtaining vehicle operating status evolution trend characteristics; obtaining vehicle driving recorder monitoring images and vehicle dynamic environment monitoring parameters, and performing multi-target visual detection and dynamic operating scene fitting to construct a vehicle operating status scene graph; performing three-dimensional scene point cloud modeling on the vehicle operating status scene graph, and performing dynamic vehicle trend rendering according to the vehicle operating status evolution trend characteristics, thereby obtaining a dynamic vehicle scene twin model. The present invention realizes efficient and intelligent vehicle data decision analysis, and improves the safety and stability of vehicle driving.

Aspects presented herein relate to perception data collection and curation. In one aspect, a network entity receives, from a first set of user equipments (UEs), a first set of perception data collected by the first set of UEs. The network entity configures, for a second set of UEs based on the first set of perception data, at least one of: a set of planned routes and time plans for collecting a second set of perception data or a set of embeddings centroids. The network entity receives, from the second set of UEs, the second set of perception data based on at least one of the configured set of planned routes and time plans or the configured set of embeddings centroids.

An agricultural vehicle includes cameras and a LIDAR sensor operably coupled to the agricultural vehicle, and an imaging controller operably coupled to the LiDAR sensor and the cameras. The imaging controller includes at least one processor, and instructions that cause the processor to receive image data from the cameras, receive LiDAR data from the LiDAR sensor, analyze the image data from each of the cameras to generate labeled image data, analyze the LIDAR data to generate labeled LiDAR data, and fuse the labeled image data with the labeled LIDAR data. Related agricultural vehicles and methods are also disclosed.

The invention relates to the technical field of river pollution identification, in particular to a pollutant identification and early warning method and system for a river patrol pollution source, and the method comprises the steps: constructing a three-dimensional monitoring network system, so as to build a three-dimensional data collection architecture; establishing a space-time reference unified framework to realize synchronous time service of different monitoring nodes; deploying an edge computing node, and implementing data preprocessing at an equipment end of the monitoring node; constructing a pollutant feature library which comprises spectral features, water quality parameter correlation features and visual morphological features of river pollutants, and establishing a water quality parameter correlation model of organic pollutants and inorganic pollutants, which comprises a plurality of feature dimensions; and a self-adaptive threshold early warning mechanism is established, a space-time composite early warning model is constructed, and accurate positioning and hazard degree grading early warning of pollution events are realized. According to the invention, through the hierarchical fusion model of technology fusion, the precision and speed of identifying the river pollutants are improved.

A small-size vehicle detection deep learning model based on feature fusion of multi-scale modules is provided, which solves the problem of small-size vehicle image detection. The model includes a Backbone network, a Neck layer and a Head network, wherein a C2f_DCNv3 module based on the combination of deformable convolution v3 (DCNv3) and a cross stage feature fusion (C2f) module and an SPPF_LSKA module based on the combination of a spatial pyramid pooling fast (SPPF) layer and a large separable kernel attention (LSKA) module are introduced into the Backbone network; a C2f_SCConv module based on the combination of spatial and channel reconstruction convolution (SCConv) and a C2f module is introduced into the Neck layer; and a multi-scale kernel detection (MSK_Detect) module is introduced into the Head network.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

Various embodiments of the present disclosure relate to operator assistance based on extracting natural language characters from one or more sensed objects. For instance, particular embodiments may generate a natural language utterance based on extracting natural language text in a nearby traffic sign. In an illustrative example, particular embodiments may detect, via object detection and within image data, one or more regions of the image data depicting the traffic sign. Particular embodiments can then extract one or more first natural language characters represented in the traffic sign based at least on performing optical character recognition within the one or more regions of the image data in response to detecting the one or more regions of the image data depicting the traffic sign.

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a vehicle multi-modal trajectory prediction method based on an improved attention network, and belongs to the technical field of intelligent vehicle trajectory prediction, and the method comprises the steps: collecting historical trajectory data of a target vehicle and surrounding vehicles as an input sequence; secondly, constructing a vehicle multi-modal trajectory prediction model which comprises a motion feature extraction module, a space-time interaction module, a space-time fusion module and a trajectory output module; the motion feature extraction module uses a multi-scale convolution attention network and a gating circulation unit for processing, the space-time interaction module uses a dynamic graph attention network for extracting vehicle interaction information, and the space-time fusion module splices and fuses target vehicle motion features and space-time interaction features to obtain space-time fusion features; the track output module inputs the fusion features into a gating circulation unit, decodes the fusion features and then inputs the fusion features into a mixed density network, and multi-mode output of vehicle tracks is achieved; and finally, a proper loss function is selected for training, so that the prediction precision and the convergence speed of the model are improved.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention relates to the technical field of battery swapping for electric vehicles. A control method of a smart battery swapping station includes: acquiring vehicle information of a vehicle requiring battery swapping, and retrieving a model type of a corresponding battery pack locking and unlocking hole and specific parameters from a database; when the vehicle arrives at a battery swapping operation platform, capturing an image of a vehicle chassis to determine an initial pose of the vehicle chassis; processing the initial pose to extract center coordinates of the locking and unlocking hole and a normal vector direction of the locking and unlocking hole; comparing the center coordinates of the locking and unlocking hole and the normal vector direction of the locking and unlocking hole with the retrieved model type of battery pack locking and unlocking hole and specific parameters in the database to determine if the information is consistent.

The invention relates to a remote sensing target detection method and device and a medium, and the method comprises the steps: inputting a feature map to a backbone network for feature extraction, inputting an extracted feature tensor into a multi-branch expansion convolution structure, and extracting multi-scale features through convolution kernels with different expansion rates. Then, multi-scale features are fused through a space and channel double-path attention mechanism, and enhanced features are generated; and the enhanced features are further input into a cascade pooling module to generate multi-level reconstruction features, and weight coefficients are calculated through a gating fusion network to carry out weighted fusion, so that multi-scale fusion features are obtained. Next, these features are input into an asymmetric decomposition convolutional layer for downsampling, and dynamic channel attention calibration is performed to generate channel enhanced features. And finally, inputting the feature map processed by the backbone network and the neck network into a detection head network, and outputting a target bounding box and category prediction. According to the method, high-precision detection of multi-scale rotating targets and high-density small targets is realized in a complex remote sensing scene.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention discloses a remote sensing monitoring method and system for grassland biomass, and the method comprises the following steps: constructing a high-dimensional feature matrix representing the state of a grassland ecosystem through employing a multi-mode remote sensing data fusion technology; based on the high-dimensional feature matrix and the dynamic spatio-temporal feature analysis framework, generating spatio-temporal correlation parameters of the grassland biomass; in combination with a deep neural network and a multi-task optimization algorithm, training a nonlinear biomass prediction model; carrying out conjoint analysis on a prediction model result and a high-resolution image, and carrying out hierarchical processing on a heterogeneous grassland region by applying a region segmentation technology; performing uncertainty propagation analysis on the partition estimation result and the multi-modal data, and outputting an optimized grassland biomass estimation result; through a cooperative monitoring mechanism of the unmanned aerial vehicle and satellite remote sensing, combining a partition estimation result with cross-spatio-temporal scale data, and dynamically updating spatio-temporal correlation parameters; and according to the optimized monitoring data, generating a grassland biomass spatial distribution map and a dynamic change trend analysis result.

The present disclosure provides an object-oriented method for identifying and classifying surface lithology in a hyperspectral remote sensing image. The method includes: determining a hyperspectral remote sensing image in a research area and a lithology type label corresponding to each hyperspectral remote sensing image, and preparing a hyperspectral remote sensing dataset; dividing pixels of the hyperspectral remote sensing image in the hyperspectral remote sensing dataset into a training set and a test set through a division strategy for a dataset without leakage information; and based on a deep learning method, extracting and fusing, through a double-branch multi-scale dual-attention mechanism network based on the training set and the test set, a spectral feature and a spatial feature of a hyperspectral remote sensing image to be tested, to generate a fused feature for representing a surface lithology type of the hyperspectral remote sensing image to be tested.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

The present invention discloses a method and system for detecting foreign objects on a track, wherein the method comprises: obtaining a multispectral image under a track environment, wherein the multispectral image comprises a visible light image and an infrared light image. Acquiring the acceleration value and the angular velocity value during the operation of a train, and judging the motion state of the train according to the acceleration value and the angular velocity value. According to the motion state of the train, performing motion compensation and deblurring processing on the multispectral image to generate a motion-compensated multispectral image. Performing adaptive image enhancement processing on the motion-compensated multispectral image to generate an enhanced multispectral image. Extracting the geometric features and material features of the track area from the enhanced multispectral image. Thereby, the present invention significantly improves the accuracy and real-time performance of foreign object detection on the track, and provides strong support for ensuring the safe operation of rail transit.

The invention discloses a forest fire detection and disappearance method and system, and relates to the technical field of forest fire prevention and data processing, and the method comprises the steps: carrying out the fire inspection of a planned forest region through a plurality of inspection unmanned planes, and transmitting an image and airborne sensor data back to a command center in real time; identifying a fire behavior by using an artificial intelligence method, and performing fire behavior judgment and fire hazard grade division; the command center screens fire points according to the thermal images and updates the positions of the fire points in real time; the fire scene spreading trend is judged according to the returned data, and a basis is provided for the dispatching task of throwing the fire extinguishing bombs by the fire extinguishing unmanned aerial vehicle; using an MAPPO multi-agent reinforcement learning algorithm to train an optimal dynamic fire point collaborative disappearance regulation strategy; and after the open fire is extinguished, the inspection unmanned aerial vehicle continues to inspect to identify a re-ignition point or a smoke point, and secondary large-area fire is prevented. According to the method, efficient collaborative fire extinguishing, dynamic environment adaptation and resource optimization configuration can be achieved, and robustness is kept when part of intelligent agents fail.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention relates to the technical field of vehicle safe driving detection, and provides a mining vehicle safe driving detection system based on vision and radar fusion, which comprises a camera module for extracting image edge gradient and road surface color features to generate a composite feature map, dynamic recognition of rigid or soft obstacles is achieved by combining connected domain marking and area mutation rate analysis, and meanwhile a pit candidate area is detected through a closed annular edge structure and color variance; and furthermore, through radar point cloud data, barrier reflection intensity abnormity and pit height difference characteristics are verified, and finally, multi-modal data are fused to generate a warning area and distance information thereof. Through a cooperative mechanism of visual feature pre-screening and radar data verification, the accuracy of obstacle detection in a complex mine environment is effectively improved, and thus the adaptability of safe driving of mining vehicles is improved.

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.