604 results about "Lidar data" patented technology

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

Embodiments of the present disclosure relate to multi-view LIDAR perception with motion cues for autonomous and semi-autonomous machines and applications. A DNN may be used to detect objects, a navigable space, weather or surface conditions, artifacts, and / or other parts or features of an environment based on multiple views of LIDAR data from multiple time slices. The DNN may include multiple input channels for processing multiple views of sensor data from multiple time slices to provide motion cues, and the extracted features from the different time slices may be geometrically projected from a first 2D view to a second 2D view, combined with features that were extracted from the second 2D view, and applied to a subsequent stage of the DNN. The data generated by the DNN may be provided to the drive stack of an autonomous vehicle or other ego-machine to enable safe planning and control of the vehicle.

The invention discloses a quadruped robot inspection method based on multi-modal sensing fusion. The method comprises the following steps of: 1, initializing a global task, loading a basic navigation map, and generating the basic navigation map comprising topographic features, forbidden areas and parking positions; 2, autonomous navigation and dynamic correction of positioning deviation are realized according to laser radar SLAM data, and the positioning deviation is dynamically corrected according to real-time observation data; 3, dynamically switching or adjusting the gait strategy according to the terrain category, training the gait strategy of the quadruped robot to be matched with the terrain category, and generating a self-adaptive motion control instruction to adapt to the terrain in real time; and step 4, synchronously realizing parking area structured data acquisition and dynamic abnormal information perception through multi-sensor fusion, and realizing target state monitoring and abnormal event response in the inspection task. According to the invention, through collaborative innovation of the bionic motion platform and multi-mode intelligent detection, all-terrain coverage, total-factor perception and full-process autonomous intelligent inspection in a complex parking lot environment is realized.

The invention discloses a mine semi-autogenous grinding process analog simulation method and system based on discrete elements, and relates to the field of analog simulation, and the method comprises the steps: according to a crushing characteristic label and a parameter mapping matrix, fusing Internet of Things sensor data, constructing a particle size distribution prediction model and an energy consumption optimization scheduling strategy, and obtaining an energy-saving operation plan; comparing the simulation data set with field production data, adjusting multi-phase coupling particle model parameters, generating a calibration parameter set, researching the influence law of semi-autogenous mill operation parameters by adopting a control variable method based on the calibration parameter set, obtaining a performance index database and an analysis report, and fusing multi-sensor data and laser radar data to obtain a semi-autogenous mill operation parameter analysis result. An ore grinding process sensing mechanism and optimization control logic are constructed, and a visual interface is generated; through multiphase modeling and data driving optimization, the simulation precision and the process suitability are comprehensively improved, and an efficient and economical solution is provided for the mine semi-autogenous grinding process.

The invention relates to the technical field of computer vision and multi-sensor data fusion, in particular to a multi-sensor image fusion obstacle real-time detection and tracking system, which comprises the following steps of: firstly, fusing data of a camera, a millimeter wave radar and a laser radar, and extracting and fusing multi-modal features; performing multi-target tracking based on a recurrent neural network: generating a target state through space-time modeling, associating a target with a historical track by using an attention mechanism, and maintaining track consistency; and the system performs semantic classification and interaction analysis on the obstacle, predicts the movement track of the obstacle, realizes deep semantic understanding, and finally outputs the identity label and the complete historical track of the obstacle.

The invention belongs to the technical field of electrical equipment detection, and discloses a defect image enhancement method fusing reasoning and generation, which integrates visible light, infrared and laser radar data through a multi-modal feature fusion network, breaks through the limitation that a contrast file CN114281093A only depends on a visible light image, and improves the detection accuracy. The dynamic attention mechanism can flexibly deploy visual, spatial and semantic feature weights according to defect types, key features can still be captured in complex environments such as strong light and shielding, and meanwhile, the spatial form of the defects is analyzed by means of three-dimensional point cloud; by means of the design, missing detection caused by insufficient characteristics of tiny parts such as hardware fittings and pins is effectively avoided. Aiming at the problem of distortion of a sample generated by a traditional data enhancement method in a comparison file, the sample quality is guaranteed through double mechanisms of reasoning constraint and physical verification, defect features output by a reasoning model directly constrain feature distribution of the generated sample, and meanwhile, a material mechanics rule is introduced to verify the physical rationality of the generated sample.

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

The invention discloses a cascading filtering positioning method and device in a complex and severe environment of a well mine and a medium, and achieves a high-precision positioning function of a well mine unmanned vehicle based on a cascading filtering method. According to the method, the IMU, the wheel speed and the wheel rotation angle are subjected to data fusion through adaptive Kalman filtering, the influence of non-Gaussian noise on wheel speed measurement is reduced through non-Gaussian noise feature extraction, and robust prior positioning information is obtained. According to the method, a regular term representing feature degradation is constructed, a laser radar observation model based on a prior map and laser radar data is constructed, and finally, a result of the laser radar observation model and prior positioning information are fused according to the regular term under a regularization Kalman filtering framework to obtain posterior positioning information. The precision and robustness of the method meet the positioning requirements of the mine unmanned vehicle.

The invention discloses a target trajectory prediction method and system based on radar detection, and a computing device, relates to the technical field of radar detection, and solves the problem that important details in prediction of a running trajectory of an enemy target automobile are excessively lost due to the fact that an application scene of first filtering and then downsampling operation is not considered in the prior art. And the technical problem of prediction deviation is solved. The method comprises the following steps: collecting laser radar data through a laser radar sensor; converting the laser radar data into initial point cloud data; judging whether the real-time running scene of the target automobile is a preset dense scene or not; if yes, carrying out downsampling and then filtering operation on the initial point cloud data; if not, filtering the initial point cloud data and then downsampling the initial point cloud data; taking the preprocessed point cloud data as input of a trajectory prediction model to obtain trajectory parameters corresponding to a plurality of trajectories of the target automobile in a set period; acquiring a target trajectory based on the trajectory parameters; according to the invention, the prediction precision and accuracy are improved.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel laser radar three-dimensional dynamic monitoring method. Aiming at the problems that in the prior art, data of a laser radar is easy to interrupt or unreliable under a severe working condition, and monitoring continuity is poor due to insufficient multi-source data fusion, the method is characterized in that laser radar surface data and optical strain gauge point data are synchronously acquired; constructing a roadway digital twinborn model; carrying out fusion and assimilation on the multi-source data by utilizing a Transform mechanism to obtain initial roadway deformation data; judging the credibility of the laser radar data based on Bayesian estimation; when the data are missing or uncredible, deducing full-field deformation by utilizing a gating mechanism in combination with optical strain gauge point data and a digital twin model; and finally, generating and outputting a three-dimensional dynamic deformation field. The system is mainly used for real-time, continuous and high-reliability deformation monitoring and safety early warning of the coal mine tunnel.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.

The invention discloses a ground feature classification method based on a frequency-space collaborative learning hierarchical fusion network. The method comprises the following steps: step 1, data preparation; step 2, the HMFE module extracts frequency domain features; step 3, extracting spatial domain features by an MLSA module; 4, the AWF module dynamically integrates the features; 5, executing a classification task; the MLSA module strengthens cross-scale interaction through a tree fusion structure and a cross attention mechanism, realizes efficient fusion of HSI and LiDAR data in a spatial domain, improves classification consistency of complex scenes, and reduces boundary blur phenomena; the HMFE module introduces a learnable frequency coding mechanism into the Mmba module so as to enhance spectrum-frequency components with strong discrimination; the AWF module realizes dynamic integration of spatial domain and frequency domain features through adaptive weighted fusion, fully mines deep complementarity of the spatial domain and the frequency domain, and improves the utilization efficiency of the model for heterogeneous data.

Systems and methods are provided for time resolution of signals produced by the emission of energy. More particularly, systems and methods are provided for measuring distance using photons propagating in a scattering medium to produce multi-dimensional, measurements of objects in a media and / or the media itself by virtue of the character of light spatially scattered and absorbed in the media resulting from the transmission of light into the media, such transmitted light having some temporal character that distinguishes it from background light, e.g., ambient sources. A method for obtaining terrestrial LiDAR data generally includes: providing a LiDAR system moving traverse to a ground canopy; emitting light pulses from the LiDAR system toward the ground canopy and terrain such that the light pulses reflect therefrom; and receiving the reflected light pulses at the LiDAR system; wherein the LiDAR system includes a monolithic module comprising a photonic device, a sampling module, a digitizing module, and a readout integrated circuit (ROIC).

The invention discloses an unmanned aerial vehicle route automatic planning system based on AI identification, and relates to the technical field of unmanned aerial vehicle route planning and obstacle avoidance. The unmanned aerial vehicle route automatic planning system based on neural network identification processes camera and laser radar data in real time through a lightweight convolutional neural network of an environment sensing module; accurate identification and classification of dynamic obstacles are realized, the perception ability in a dense city environment is effectively improved, and the risk of obstacle avoidance failure caused by sensor data updating delay is reduced. The fusion and tracking module adopts a space-time alignment and multi-source data fusion technology to generate uniform occupation representation and motion trail, so that the system can adapt to sudden obstacle change, the dependence on a preloaded map is reduced, and the navigation reliability in an unknown or dynamic scene is enhanced; the path planning module integrates a reinforcement learning algorithm, takes a dynamic obstacle state as input, and optimizes path generation through a multi-target reward function.

Techniques for determining a calibration score associated with a sensor are discussed herein. For example, a lidar sensor may capture lidar data representing lidar returns in an environment. A number of data points associated with multiple regions can be determined based on one or more lidar scans associated with the sensor. For example, a number of lidar returns for each region may be used to determine a calibration score of the lidar sensor indicating an accuracy of the lidar sensor in a variety of conditions.

The invention, which relates to the technical field of intelligent traffic and unmanned aerial vehicles, discloses an unmanned aerial vehicle-based automatic identification system for a thrown object on a highway, comprising: an unmanned aerial vehicle cluster deployment module which adopts a Mesh ad hoc network and covers a lane with a width of 150-250 m; the multi-modal data acquisition module is used for acquiring infrared light, visible light, a depth map and point cloud data; the image preprocessing module is used for downsampling, filtering and denoising and enhancing the contrast ratio; the thrown object recognition module is fused with multi-modal features, the confidence coefficient threshold value is 0.6-0.7, and suspected targets are verified through point cloud clustering; the three-dimensional modeling and positioning module is used for fusing binocular and laser radar data; the intelligent decision-making and scheduling module is used for generating priorities in combination with the traffic flow and planning an inspection path; and the linkage processing module is used for pushing information through 4G / 5G, and linking the information board and the road administration vehicle for processing. The method improves the monitoring coverage rate and the recognition accuracy, shortens the response time, reduces the positioning error, optimizes the processing efficiency, and guarantees the high-speed traffic safety.

The invention relates to the technical field of data processing, in particular to an automatic driving vehicle data processing system based on identity recognition, comprising: a data acquisition module outputting an original data stream carrying a digital signature; the preprocessing module filters illegal data streams according to the digital signature, and outputs time-aligned laser radar data and visual data; the feature fusion module outputs a six-degree-of-freedom pose estimation result; the safety processing module generates safety feature descriptors according to the cosine similarity, the timestamp difference constraint range and the encryption seeds; the verification storage module stores the Merkel tree, the encrypted point cloud and the encrypted image to a candidate node group; and the calibration module calculates a correlation coefficient between the timestamp of the visual data and the timestamp of the laser radar data, and reconfigures a hardware trigger time sequence of the data acquisition module. According to the method, the problem of cross-modal data fusion credibility caused by sensor clock skew is solved, and the identity recognition accuracy is improved.

A method employs a simulation-driven environment for Al model training in filmmaking, using virtual scenes, filmmaking metadata, and Lidar data to enhance video generation capabilities. A system includes processors and memory to generate virtual scenes, integrate Lidar data with filmmaking metadata, and train Al models for improved video content creation. A computer-readable medium contains instructions for generating virtual scenes, integrating Lidar data and filmmaking metadata, and training Al models to enhance video content generation without constant new real-world data.

The invention discloses a centimeter-level positioning and intelligent obstacle avoidance system for an unmanned aerial vehicle, relates to the technical field of unmanned aerial vehicles, and solves the technical problems that the centimeter-level positioning requirement is difficult to meet, and the intelligent degree of an obstacle avoidance strategy needs to be improved in a complex and changeable environment. The high-precision positioning module is used for acquiring multi-source sensor data; the self-developed multi-satellite positioning algorithm module generates initial positioning information by adopting a self-developed multi-mode GNSS fusion algorithm and an RTK technology, combines a Kalman filtering algorithm, collaboratively optimizes state estimation by predicting and updating GNSS data, and fuses visual SLAM and laser radar data at the same time; the cloud optimization and large model prediction module uploads unmanned aerial vehicle positioning data to a cloud, and models a self-developed multi-satellite positioning algorithm by using a deep learning model; and the intelligent obstacle avoidance decision module identifies obstacle types and generates an environment perception map through deep learning based on the fused positioning data and the high-precision map.

The invention discloses a concrete wall full-section deformation monitoring device and method, and relates to the technical field of engineering structure health monitoring, and the device comprises an integrated measurement unit, a passive stable characteristic target group and a processing unit. The integrated measuring unit is composed of an area array solid-state laser radar and a high-resolution binocular vision camera which are rigidly and fixedly connected, and is used for synchronously collecting three-dimensional point cloud and two-dimensional images of a wall and an environment. The target group is arranged on an independent stable object near the wall body; the processing unit identifies coordinates of the target group based on laser radar data, establishes a world coordinate system, calculates self pose drift of the measuring unit by periodically detecting position change of the target, and performs coordinate correction on a wall surface relative displacement field obtained by visual calculation by using the drift distance; and finally, outputting a full-section absolute deformation field under a world coordinate system. According to the invention, high-precision monitoring of full-field continuous deformation of the concrete wall is realized, and the reliability and comprehensiveness of structural health monitoring data are improved.

The invention discloses an intelligent shuttle vehicle control system for tray conveying, which belongs to the technical field of intelligent shuttle vehicle control and comprises a multi-mode sensing module S1, a dynamic path planning module S2, a multi-vehicle collaborative scheduling module S3, an energy efficiency optimization module S4 and an edge calculation module S5. The multi-mode sensing module S1 comprises an environment sensing sub-module S11 and a cargo state monitoring sub-module S12, the environment sensing sub-module S11 comprises laser radar data preprocessing and visual sensor calibration and fusion, and the laser radar data preprocessing adopts a filtering algorithm based on statistics to remove outliers generated by environment interference. Through the distributed consensus algorithm and the task allocation strategy, the system throughput is improved, the path conflict rate is reduced, the work waiting time is remarkably shortened, the improved A * algorithm is combined with the reinforcement learning model, the time consumption of single path planning is shortened, the path generation efficiency is improved, and the method adapts to the high-dynamic storage environment.

The invention discloses a dynamic scene laser mapping and positioning method based on semantic information visual enhancement, and belongs to the field of navigation positioning. Comprising the steps that observation data of multiple sensors are acquired, and the multiple sensors comprise a laser radar and a visual sensor; establishing a coordinate conversion relation of the visual sensor relative to the laser radar according to the observation data; according to the coordinate conversion relation, mapping the visual semantic information to a laser radar coordinate system to obtain semantic enhancement data; according to the semantic enhancement data, pose estimation of the robot is updated under a particle filtering framework; constructing a composite map containing semantic information and geometric information according to the updated pose estimation; and executing a navigation task according to the composite map. Through deep fusion of visual semantic information and laser radar data, the positioning precision and navigation efficiency of the robot in a dynamic scene are effectively improved, and reliable technical support is provided for intelligent upgrading of the manufacturing industry.

Embodiments of this application provides a LiDAR data processing method and apparatus, the method comprises: emitting laser beams multiple times; sampling echoes of a laser beam emitted for one time by at least two receiving blocks to obtain sampling data of the at least two receiving blocks for the laser beam emitted for one time respectively, a first receiving block in the at least two receiving blocks is used to sample a laser beam emitted for an Nth time and a laser beam emitted for an (N+1)th time; obtaining a point cloud data of the laser beam emitted for one time; and generating a frame of point cloud data based on the point cloud data of the laser beam emitted for one time corresponding to at least one of the receiving blocks, which can reduce the cost of LiDAR.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention discloses a quadruped robot space precise positioning method and system based on deep learning. The quadruped robot space precise positioning method specifically comprises the steps that S1, a laser radar data stream and an inertial measurement unit data stream are aligned to generate a synchronous sensing data sequence in a unified mode; s2, determining a mapping pose sequence through consistency evaluation of poses output by the offline point cloud registration algorithm pool; s3, performing parallel loopback detection to generate a unified loopback constraint set; s4, the mapping pose sequence, the loopback constraint set and the pre-integration constraint are written into the factor graph model to generate a three-dimensional point cloud reference map; s5, the local point cloud frame and the three-dimensional point cloud reference map are registered, and a map matching pose is output; s6, the map matching pose and the pre-integration result are written into a factor graph model to continuously solve and output a continuous-time six-degree-of-freedom pose result; and S7, outputting a six-degree-of-freedom pose result in continuous time. According to the invention, direction constraint consistency point cloud registration and dual-channel loopback optimization are introduced, and stable and high-precision positioning of the closed space is realized.

The invention provides a seismic exploration path planning method, system and device and a medium, and relates to the technical field of seismic exploration assistance, and the method comprises the steps: a laser radar module obtains laser radar data of a target region; the fusion correction module obtains current attitude information and current positioning information of the unmanned aerial vehicle, and determines target three-dimensional point cloud data according to the current attitude information, the current positioning information and the laser radar data; the data processing module determines earth surface types and terrain gradients respectively corresponding to different sub-regions in the target region through a machine learning algorithm based on the target three-dimensional point cloud data; constructing a three-dimensional trafficability map of the target area based on the earth surface type and the terrain gradient of each sub-area; and the path planning module performs path planning by adopting a preset path planning algorithm based on the three-dimensional trafficability map to obtain a target exploration path, and the target exploration path is a transportation path of the target equipment. According to the invention, the planning precision and the planning efficiency are improved.

The invention discloses a self-distillation map attention-enhanced multi-source remote sensing image fusion classification method. The method comprises the steps of obtaining hyperspectral data and laser radar data of a to-be-classified earth surface region; inputting the hyperspectral data and the laser radar data into a pre-trained semi-supervised self-distillation diagram attention enhancement network for feature extraction and fusion to obtain fused features; and classifying and outputting the fused features through a classifier to obtain fusion prediction probability distribution, and taking a category corresponding to a maximum probability value in the fusion prediction probability distribution as a final classification result. According to the method, complementary information of different modes can be effectively utilized, the distinguishing capability of the classifier is remarkably enhanced under the condition that dependence on prior knowledge is reduced, and ground feature types can be better recognized.