440 results about "Hyperspectral image classification" patented technology

The invention discloses a hyperspectral image classification method based on proxy attention and a VIT architecture, and belongs to the field of image classification, and the method comprises the following steps: preprocessing hyperspectral image data, and dividing the hyperspectral image data into a training set and a test set; constructing a hyperspectral image classification model based on proxy attention and a VIT architecture; training the hyperspectral image classification model by adopting the training set; and inputting the hyperspectral data in the test set into the trained hyperspectral image classification model, reasoning to obtain a prediction category, and evaluating the obtained prediction category through an evaluation index. According to the invention, the agent attention encoder based on agent attention is constructed, hierarchical feature extraction is carried out through the agent attention encoder of L-layer cascade, layer-by-layer fusion and enhancement of space-spectrum information in an initial feature coding matrix are realized, the calculation complexity is significantly reduced while the strong representation capability is maintained, and the method has the advantages of high efficiency, high resolution and the like. And good balance between the calculation efficiency and the feature expression capability is realized.

The invention provides an open set cross-domain hyperspectral classification method and system based on multi-mode and optimal transmission, and relates to the technical field of hyperspectral image classification. The method specifically comprises the following steps: acquiring and utilizing an original hyperspectral image to construct a labeled source domain data set and an unlabeled target domain data set; utilizing a spatial-spectral feature extractor to respectively obtain spatial-spectral features of the source domain data and spatial-spectral features of the target domain data; and determining a source domain prototype, calculating a similarity matrix between the target domain data and the source domain prototype, solving an optimal transmission matching matrix, distributing a pseudo tag for each target domain data, and performing text correction on all the source domain data and all the target domain data by using a contrast learning loss function to obtain the target domain data. And the source domain prototype and the spatial spectrum feature extractor are optimized by constructing the total loss of minimizing hyperspectral image classification, so that classification prediction can be carried out on the hyperspectral image to be classified, and target domain data can be classified more accurately.

The invention discloses a hyperspectral image classification method based on an S2CFM-spatial spectrum convolution fusion Mama network model, and the method employs a parallel double-branch structure to extract the spatial context information and spectral sequence features of a hyperspectral image, and finally integrates the features through a dynamic convolution fusion module, thereby achieving the classification of the hyperspectral image. The method solves the problem of unbalanced utilization of space-spectrum information in a traditional method, introduces a space spectrum convolution fusion Mama network, and fuses a multi-scale convolution block (MCB), a dynamic convolution block (DCB) and a space spectrum Mama block (S2MB); the method solves the problems that in the prior art, the capacity of CNN for capturing spectral information is relatively limited, and a complex spectrum-space characteristic relation in hyperspectral data is difficult to fully represent; the problems that in HSI data, due to the fact that the dimensionality is high and the sample size is limited, an over-fitting problem is prone to occurring, and the generalization performance of the HSI data is limited are solved through a Transformers-based architecture.

The invention discloses a construction method of a self-adaptive gated spectrum-space-graph collaborative fusion network. The construction method comprises the following steps: step 1, constructing a network overall architecture; step 2, spatial branching-hierarchical spatial feature modeling is carried out; step 3, spectrum branching-adaptive spectrum feature optimization; step 4, designing an adaptive gating fusion module AGFM; the invention provides a spectrum-space-graph collaborative fusion network, which is a double-flow architecture, and solves the problems of complex space-spectrum interactive modeling, spectrum redundancy and low calculation efficiency in hyperspectral image classification. According to the network, through integration of hierarchical spatial feature learning, adaptive spectrum optimization and a dynamic cross-modal fusion mechanism, complementary advantages of a graph attention mechanism, intelligent agent self-attention and data-driven spectrum modeling are effectively combined; experimental results on three reference data sets verify the advanced performance of the SGCFN, and ablation studies prove that each module has an irreplaceable effect on enhancing classification robustness.

The invention discloses a cross-scene hyperspectral image classification method combined with channel-space attention improvement, and belongs to the field of remote sensing image classification. According to the method, the problems of poor classification precision, to-be-improved robustness and limited feature extraction capability caused by insufficient space-spectral feature relevance modeling and weak cross-scene generalization capability of a traditional model are solved. A domain generalization method is introduced to construct a feature alignment module, data distribution differences between different scenes are reduced through a distribution adaptation algorithm, and the model generalization ability is improved; acquiring local space and global spectral features of the hyperspectral data by using a space-spectrum feature extraction module, and enhancing discriminative spectral feature extraction through a channel-space attention fusion network; through fusion of a Lion optimizer and a cosine annealing strategy, global optimization is realized, training stability is guaranteed, and a feature learning effect is improved. The method can be applied to remote sensing image classification.

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 provides a frequency domain Mama multi-scale hyperspectral image classification method and device, and relates to the technical field of hyperspectral image classification. The method comprises the following steps: collecting hyperspectral image data, preprocessing the hyperspectral image data, and segmenting the hyperspectral image data into a plurality of image blocks; inputting the image blocks into a space spectrum interaction feature extraction module, and extracting interaction features through a space branch and a spectrum space fusion branch; inputting the interaction features into a frequency domain disturbance embedding module to obtain features after frequency domain processing; inputting the features after frequency domain processing into an adaptive bidirectional Mama module, and extracting forward and backward spatial features and spectral features; and performing multi-level integration on the spectral features and the spatial features in different stages to obtain global features, and realizing final classification. According to the hyperspectral image classification method, the accuracy and robustness of hyperspectral image classification can be remarkably improved, and higher generalization ability and stability are shown in different data sets and complex scenes.

The invention provides a hyperspectral image classification method based on a state space model. The problem that an existing method is poor in classification precision is mainly solved. According to the scheme, the method comprises the following steps: 1) inputting a hyperspectral image, and preprocessing the hyperspectral image; 2) eliminating spectrum variability by using a neighborhood spectrum fusion method, sensing spectrum detail differences by using a sub-spectrum scanning method, and obtaining image spectrum classification information; 3) constructing a spatial feature extraction network by using an SS2D layer, an improved spatial attention mechanism and a plurality of residual blocks, and acquiring spatial classification information of the hyperspectral image by using the network; and 4) fusing space and spectrum classification information at a decision layer to obtain a final classification result. According to the method, the influence caused by spectrum variability can be reduced in a relatively small range, local spectrum information is not ignored on the premise that comprehensive spectrum information can be perceived, and spatial information is combined, so that the classification effect of the hyperspectral image is effectively improved.

The invention discloses a hyperspectral image classification method of a cross-hop node interaction graph attention network. The method comprises the following steps: step 1, constructing a DNIGAT-CFF overall model architecture; 2, differentiated features are extracted based on the coupled convolution blocks; step 3, cross-hop node interaction graph attention network enhanced spectrum-spatial feature learning; step 4, carrying out multi-scale cross guidance feature fusion CGFF; step 5, important fusion features are highlighted by a weighted attention mechanism; according to the method, the attention network of cross-hop node interaction is constructed, interaction between nodes with different hop counts is effectively utilized, and the extraction capability of spectrum and spatial features is enhanced; a multi-scale cross guide feature fusion module is adopted, complementarity and correlation between different scale features are fully considered, and effective fusion of the multi-scale features is achieved; and in combination with a weighted attention mechanism, important features in the fused multi-scale features are highlighted, so that the precision of hyperspectral image classification is improved.

The invention provides a hyperspectral image open set classification method based on multi-scale convolution and feature fusion, and aims to improve the classification precision of a known category and an unknown category in a hyperspectral image and the robustness of a classification model. According to the method, spectral and spatial features are fused through a deep learning model, and the problems of unknown category identification and feature optimization in hyperspectral image classification are solved. Firstly, spectral features are extracted through multi-scale convolution, and in combination with a channel attention mechanism, capture of most discriminative spectral information is enhanced. And secondly, through a multi-branch structure and a convolution enhancement technology, the expression of spectral features is optimized, and the perception capability of complex spatial information is improved. And the spatial features are enhanced through a high-frequency enhancement technology, so that the sensitivity to detail and edge information is improved. Through the innovative feature extraction and fusion strategy, the classification precision of the hyperspectral image open set and the robustness of the classification model are improved.

The invention belongs to the technical field of agricultural hyperspectral image classification, and discloses a non-optically active water quality parameter inversion method based on a hyperspectral image. The system comprises a data preprocessing module, a spectrum-spatial feature fusion module, a multi-algorithm collaborative inversion module and a visual output module. The collected hyperspectral images are input into the system, feature learning is carried out by fusing a Transform architecture and a Diffusion data generation technology, parameter optimization is carried out by combining machine learning algorithms such as a random forest and XGBoost, and a high-precision water quality inversion model is obtained after iterative training; and inputting a hyperspectral remote sensing image to be analyzed, and outputting a spatial distribution map of the non-optical activity water quality parameters to realize intelligent inversion of the water quality parameters. According to the method, spectral feature association is mined based on a self-attention mechanism of deep learning, the data characterization capability is enhanced through a diffusion model, the generalization performance of the model is improved through multi-algorithm collaborative optimization, and the efficiency of large-range water area monitoring and the parameter inversion precision are improved.

The invention discloses a self-supervised hyperspectral image classification method suitable for a low-annotation sample scene, and relates to the technical field of hyperspectral remote sensing image processing, comprising a self-supervised category sensing network oriented to the low-annotation scene; in the pre-training stage, a grouping spectrum enhancement module, a spectrum self-attention module and mask reconstruction are adopted, and the model is guided to focus on category-sensitive space-spectrum features under the label-free condition by minimizing the difference between a reconstructed image and an original shielded area; in the fine tuning stage, pre-trained network parameters are used as initialization parameters, and feature expression is further refined through classification loss. Therefore, by adopting the self-supervised hyperspectral image classification method suitable for the low-label sample scene, the lossless transmission of difficult sample features is realized, the distinguishing feature expression of mixed pixels is enhanced, and the classification balance of few sample categories is improved.

The invention discloses a hyperspectral image classification method and system based on a double-branch lightweight algorithm, belongs to the technical field of computer vision and image processing, and solves the problems that an existing image classification model is complex in structure, so that the calculated amount is large, the occupied memory is high, and balance between classification precision and model lightweight is difficult to achieve. The method comprises the steps of collecting a hyperspectral image, preprocessing the hyperspectral image, performing iterative training on a double-branch lightweight algorithm model based on a training set, executing the double-branch lightweight algorithm model, and classifying a test set by the double-branch lightweight algorithm model. According to the hyperspectral image classification method, the algorithm complexity is greatly reduced, the high precision of hyperspectral image classification is ensured, the lightweight space-spectrum Transformer is introduced into the double-branch lightweight algorithm model, and through the optimization design of the whole structure and the lightweight efficient Attention-Aware mechanism, the classification accuracy of the hyperspectral image is improved. The complexity of the model is reduced, and the characteristic relation between the modeling space and the spectrum sequence can be better established.

The invention discloses a spectrum-space depth fusion hyperspectral image classification method oriented to a small sample condition, and the method comprises the steps: firstly carrying out the multi-scale hole convolution processing of input hyperspectral data through a range attention convolution SAC module, and extracting the multi-scale context features; then, a spatial normalization attention SNA mechanism is utilized to carry out adaptive weighting adjustment of spatial dimensions on the feature map, and spatial feature representation of the key area is enhanced; the method comprises the following steps: constructing a lightweight hybrid expert model LMOE, carrying out parallel processing and gating weighting through a multi-path expert network, carrying out efficient refining and mapping on features, finally fusing processed spectral features and spatial features, and carrying out pixel-level prediction through a classifier to obtain a terrain classification result map of a hyperspectral image. The method solves the problems that in the prior art, overfitting is prone to occurring under the small sample condition, the spectrum-space collaborative modeling capacity is insufficient, the long-range dependence obtaining efficiency is low, and the recognition precision is reduced under the class imbalance scene.

The invention discloses a hyperspectral image classification method, and relates to the technical field of image processing, and the method comprises the following steps: preparing and preprocessing a data set; constructing a network model; selecting a loss function and an evaluation index; training a network model; according to the invention, the spectral feature extraction module and the spatial feature extraction module are processed in parallel, the spectral and spatial features of different receptive fields are obtained by adopting a multi-scale feature extraction mode, and the spectral and spatial features are adaptively fused by adopting the dynamic feature fusion module. The method can effectively improve the classification precision, training efficiency and generalization of the model, and has good flexibility and expandability. A proposed dynamic feature fusion module can adaptively perform dynamic cross-feature interaction based on a cross-attention module, the cross-attention module can realize efficient interaction between spectrum and spatial features based on a dynamic mechanism, and meanwhile, the generalization ability of the model to complex scenes is improved.

The invention discloses a construction method of a multi-scale double attention network for hyperspectral image classification. The construction method mainly comprises four key stages of input processing, multi-scale feature extraction, double attention enhancement and output classification. Wherein the multi-scale feature extraction module captures features of different scales through five parallel branches, and the dual attention mechanism cooperatively completes feature enhancement of a channel dimension and a space dimension. The invention provides an innovative hyperspectral image classification deep learning framework, and the core of the framework is to organically combine multi-scale feature learning and a dual attention mechanism. According to the network, aiming at the characteristics of a hyperspectral image, extraction of space-spectrum joint features and identification requirements of targets with different scales are fully considered in the aspect of architecture design, and meanwhile, the expression ability of key features is enhanced through an attention mechanism; therefore, the network can pay attention to local details and global semantic information at the same time, and the feature expression capability and the classification accuracy are remarkably improved.

The invention relates to a semi-supervised spectral image classification method based on an auxiliary task, and the method comprises the steps: obtaining a multispectral image and a corresponding low-resolution RGB image, carrying out the super-resolution reconstruction of the low-resolution RGB image, generating a high-resolution RGB image, pairing the high-resolution RGB image with the multispectral image to form an image pair, and carrying out the data amplification; performing feature extraction on the high-resolution RGB image and the multispectral image by using two feature extraction networks with the same architecture and independent parameters, and performing feature fusion by using a dual attention mechanism to generate fused features; calculating supervision loss for labeled data, generating a pseudo label for unlabeled data, and calculating unsupervised loss in combination with a consistency regularization strategy; automatically adjusting and optimizing weight parameters and loss weights of the fusion features through a Bayesian optimization method, and updating model parameters by adopting a back propagation algorithm; and inputting the fusion features into a classification network, and outputting a spectral image classification result. The method is beneficial to improving the accuracy and robustness of spectral image classification.

The invention discloses a hyperspectral image classification method for cross-domain small sample learning based on diffusion enhancement prototype knowledge distillation, and belongs to the technical field of hyperspectral image processing. The method comprises the following steps: extracting a neighborhood data cube, aligning spectrums, dividing a support set and a query set, and applying a mask and enhancing noise; executing domain adversarial denoising and reconstruction tasks, aligning feature distribution, and outputting a pre-training encoder; decoupling features, capturing spectrum-space global and local dependency relationships, and calculating similarity between a query set and a category prototype; constructing a distillation framework to realize knowledge migration; optimizing model parameters, and introducing a signal-to-noise ratio to enhance loss suppression noise; and performing feature extraction by using the optimized student model to generate a hyperspectral image classification result. According to the method, the problems of domain offset, intra-class feature dispersion, inter-class boundary fuzziness, noise interference and the like are solved, and the classification accuracy in a small sample scene is improved.

The invention relates to the technical field of image classification, and discloses a hyperspectral image domain generalization classification method, system and device and a medium. The method comprises the following steps: acquiring a plurality of hyperspectral images of different ground feature types; obtaining a mean value and a variance of each hyperspectral image in a channel dimension, performing random disruption, and determining a spectral variation parameter of the hyperspectral image according to the mean value and the variance before and after random disruption so as to generate a corresponding spectral variation image; separating the center and the background of each hyperspectral image to obtain respective center image and background image, randomly disorganizing the center image and the background image, and generating a corresponding spatial variation image by combining the disorganized center image and background image of each hyperspectral image; a hyperspectral image classification model is obtained by training an enhanced hyperspectral image obtained by fusing a plurality of corresponding spectral variation images and spatial variation images, so that ground feature classification is performed on a to-be-classified hyperspectral image, and the domain generalization performance of the hyperspectral image classification model is improved.

The invention belongs to the technical field of hyperspectral image classification, and provides a surface feature classification method and device based on a hyperspectral image. The local object classification method comprises the following steps: S1, acquiring hyperspectral image data and preprocessing the hyperspectral image data; s2, marking the types of ground objects in the processed hyperspectral image data, and making a label file; s3, constructing and training a ground object target detection model, wherein the ground object target detection model comprises a multi-scale 3D depth separable convolution module, a dual-channel convolution block attention module and a classifier; and S4, performing target detection on to-be-classified hyperspectral data by using the trained ground object target detection model, and generating a visual image of a ground object target position and category. According to the local object classification method, by combining multi-scale 3D depth separable convolution and a dual-channel convolution block attention module, the focusing capability of the model on key features is improved, and the precision and accuracy of hyperspectral image classification in a complex scene are effectively improved.

The invention provides an incremental hyperspectral image classification method based on virtual sample expansion, and belongs to the technical field of remote sensing image processing and application. Firstly, data preprocessing is carried out on original data of a hyperspectral image; secondly, constructing a virtual sample expansion network and training the virtual sample expansion network in an initial stage; thirdly, building and training an incremental hyperspectral image classification model in an initial stage; and finally, training an incremental hyperspectral image classification model in an incremental stage by using a virtual sample expansion network and momentum knowledge distillation, performing classification and outputting a result. According to the invention, incremental learning can be carried out on the hyperspectral images collected from different hyperspectral imagers, so that the classification capability of not forgetting old classes is realized while new classes are learned; according to the method, a feasible solution is provided for realizing hyperspectral image classification in an incremental manner, so that the hyperspectral image classification theory is further improved from large-scale practical application.

The invention discloses an agricultural hyperspectral image classification method based on a hypergraph and Mama, and the method comprises the steps: S1, constructing a spectrum-space hypergraph according to an agricultural hyperspectral image; s2, a Mama framework used for hyperspectral classification is constructed; and S3, inputting the spectrum-space hypergraph into a Mama framework for hyperspectral classification, and carrying out classification, the method effectively fuses space and spectrum information, improves the precision of hyperspectral image classification, and solves the problem of information capture in a traditional method.

The invention provides a hyperspectral image classification method fusing a hybrid multi-hop graph convolutional network, and belongs to the technical field of hyperspectral image processing, and the method comprises the steps: carrying out the dimension reduction processing of original HSI image data, and obtaining the HSI image data after the dimension reduction; segmenting the HSI image data after dimension reduction to obtain a superpixel segmentation result; inputting the original HSI image data into a convolutional feature pyramid network to obtain a first feature representation, and inputting the superpixel segmentation result into a hybrid multi-hop graph convolutional neural network to obtain a second feature representation; splicing the first feature representation and the second feature representation, and inputting the spliced feature representation into a mixed feature fusion module to obtain a fused feature; and inputting the features into a softmax classifier to obtain a final classification result. Local spatial-spectral features in the superpixel can be learned, long-distance associated information of the captured image can be captured, and rich multi-scale features and semantic information in the image can be effectively extracted; and higher classification precision is realized with less parameter quantity.

The present invention belongs to the field of plant science research, and discloses a leaf hyperspectral image classification and regression method based on a multi-scale cascade convolutional neural network. First, in 3D-CNN, dilated convolution is embedded to construct a spectral-spatial feature extraction structure of different scales to achieve the fusion of multi-scale features; secondly, 1D-CNN is cascaded after 3D-CNN to further extract high-level abstract spectral features, and the proposed multi-scale 3D-1D-CNN network is explored for the optimal framework; finally, on two limited sample facility crop leaf data sets, the proposed multi-scale 3D-1D-CNN network model is compared with a benchmark model and a multi-scale 3D-CNN model to verify the effectiveness of the proposed method. The method of the present invention is helpful for the classification and regression of leaf hyperspectral images, and can also provide new ideas and technical assistance for other image classification and regression methods in the field of agricultural information technology.

The invention relates to the technical field of remote sensing image processing, in particular to a joint sparse representation hyperspectral image classification method based on dual domain constraints, which comprises the following steps: preprocessing: carrying out spectral feature-based wave band grouping on hyperspectral image data, and then carrying out MNF data dimension reduction on the grouped hyperspectral image data; extracting a main component feature map by using morphology; performing superpixel segmentation on the hyperspectral image by using an improved watershed algorithm, and performing FCM clustering on the hyperspectral image; adaptive selection of the neighborhood is carried out through weight calculation under double constraints of the obtained superpixel neighborhood and the clustering field; multi-view angles of the superpixel field, the clustering field and the constraint field are used for joint sparse representation; and a majority voting method is adopted to integrate classification results, and the classification results are adjusted through a correction rule, so that the classification effect of the hyperspectral image is improved, and the classification precision of edge pixels is improved.

The invention discloses a hyperspectral image classification model pre-training method and a classification device. The pre-training method comprises the following steps of: mapping a hyperspectral image sample into a uniaxial feature vector and then randomly covering the uniaxial feature vector; performing long-range feature analysis on the covered uniaxial feature vector to obtain a global feature; reconstructing a hyperspectral image according to the global features, calculating the loss of the global features and the reconstructed hyperspectral image, and updating the weight and bias in long-range feature analysis according to the loss; and migrating the updated weight and bias to the hyperspectral image classification model. According to the method, the problems of limited sample data and difficulty in labeling can be solved, the limitation of a private data set can be broken through, and the classification capability and generalization performance of the hyperspectral image are effectively improved.

The invention belongs to the technical field of image classification, and discloses a hyperspectral image classification method, which comprises the following steps: obtaining training data including hyperspectral image training data and corresponding image classification labels; constructing an initial image classification model, wherein the initial image classification model comprises a spectrum-space feature extraction module, a local feature extraction module and a KANLinear module which are connected in sequence; training the initial image classification model based on the training data to obtain a trained image classification model; and executing an image classification task of the to-be-classified hyperspectral image based on the trained image classification model. According to the technical scheme, the hyperspectral image classification task can be efficiently processed, the classification performance is improved, and computing resources are optimized.

The invention discloses a hyperspectral image classification method and a hyperspectral image classification device based on a double-branch multi-scale CNN (Convolutional Neural Network) and a memory enhancement Transform. The method specifically comprises the following steps: step 1, acquiring and preprocessing hyperspectral image data; and step 2, inputting the data after the step 1 into a double-branch multi-scale CNN feature extraction module, and fully extracting spatial features and spatial-spectrum combined features of the hyperspectrum. And step 3, transmitting the data after the step 2 into a memory module, performing detail memory and global memory on the data by MT1, and performing normal memory and important feature memory on the data by MT2. 4, the data obtained in the step 2 and the data obtained in the step 3 serve as input and are transmitted into a memory Transform; and 5, finally, a classification result is obtained through an mlp-head layer. According to the method, rich spatial features and spectral features of the hyperspectrum can be fully combined, and the classification precision and the classification performance of the hyperspectral image are improved.

The invention provides a hyperspectral image classification method and system based on a Swin-Transform fusion network, and relates to the technical field of image classification, and the method comprises the steps: obtaining a hyperspectral data cube with a label, cutting a three-dimensional image block with a labeling pixel as a center, and constructing an initial training sample library; constructing a dynamic adversarial amplification system and a feature extraction classification network taking a mixed Swin-Transform as a trunk, and dynamically generating a pseudo sample with high classification difficulty to expand a training set through cooperative training in an alternate circulation mode; and cutting image blocks of pixels to be classified, inputting the image blocks into the trained classification network, outputting probability distribution and determining a final category. According to the method, the problem of small sample overfitting is effectively relieved, space and spectral features are captured through a double-branch structure, adaptive fusion is carried out, and the precision and robustness of hyperspectral image classification are improved.

The invention discloses a cross-domain hyperspectral image classification method based on semantic guidance and cross-domain alignment. The method comprises the following steps: 1) processing original hyperspectral remote sensing image data and labels thereof to construct a source domain data set; 2) extracting visual features of the processed hyperspectral image through a visual feature extraction module; according to the hyperspectral image classification method, semantic information is fully utilized to establish shared knowledge between a source domain and a target domain, and the accuracy of hyperspectral image classification is effectively improved.