345 results about "Hyperspectral image classification" patented technology

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 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 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 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 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 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 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 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.

The invention relates to a hyperspectral image classification method based on BoostFormer double-view perceptual feature fusion, and belongs to the technical field of information processing. The method comprises the following steps: S1, hyperspectral image acquisition and data preprocessing; s2, inputting the training sample into a BoostFormer dual-view perceptual feature fusion network for learning; s3, calculating loss and updating model parameters; s4, after training is completed, the test samples are classified, and a test result is obtained. The performance of the method provided by the invention on a hyperspectral image classification task is superior to that of other hyperspectral image classification algorithms.

The invention provides a space-spectrum Mama hyperspectral image classification method based on a pyramid structure. The problem that an existing method is insufficient in performance in classification of complex backgrounds and fine-grained ground objects is mainly solved. Comprising the following steps: 1) acquiring a hyperspectral image, and constructing a training set and a test set; 2) designing a double-branch model based on multi-scale space-spectrum adaptive fusion, and extracting multi-scale space and spectrum feature information in parallel; 3) respectively designing a spatial feature extraction module and a spectral feature extraction module, capturing target information in a spatial domain and a spectral domain, and optimizing fusion of spatial spectral features by using a multi-scale adaptive weighting mechanism; 4) constructing a space spectrum interactive fusion module for deep interactive fusion of features extracted by space and spectrum branches; and 5) training the model until convergence, and obtaining a final classification result by using the model. The method can effectively improve the processing capability of the complex background in the image, enhance the ground feature classification precision, and significantly improve the hyperspectral image classification performance.

The invention discloses a cross-scene hyperspectral image classification method based on global-local clustering and comparative learning, and belongs to the technical field of remote sensing image classification. The problem that an existing cross-scene classification method is poor in classification performance when the categories of the source domain and the target domain are inconsistent is solved. The method comprises the following steps: extracting local morphological features and global dependency features through a morphological Transform network in combination with spectrum-spatial morphological convolution and a self-attention mechanism; a global clustering strategy and a local KNN clustering strategy are adopted, and cross-domain negative migration is reduced; contrast learning is introduced to construct cross-domain positive and negative sample pairs, the unknown category separation capability is enhanced, and the cross-scene classification robustness is remarkably improved. The method can be applied to cross-domain hyperspectral image classification scenes.

The invention discloses a self-adaptive kernel selection fusion Transform network and method for hyperspectral image classification, and belongs to the technical field of hyperspectral image classification, and the network comprises three innovative modules: (1) a self-adaptive convolution module (ACBlock) improves the calculation efficiency while guaranteeing the feature extraction effect through a cascade structure of convolution and separable convolution; (2) a kernel selection fusion attention module (KSFA) adopts multi-scale convolution and dynamic attention fusion design, and feature enhancement and dynamic weighted integration are realized through a kernel selection strategy; and (3) the dynamic Tanh (DyT) replaces the traditional normalization layer, and two-dimensional convolution is introduced to compensate the information loss of the Transform layer. According to the innovative design, the AKSFormer keeps the advantages of Transform global modeling, and meanwhile, the problems that local feature extraction and small sample generalization are insufficient in ability and the like are effectively solved. Experiments on three common data sets and one homemade data set show that compared with the existing advanced method, the AKSFormer classification precision is improved.

The invention belongs to the field of hyperspectral image classification, and discloses a hyperspectral image classification method and system based on multi-scale spatial-spectral joint representation and dynamic context modeling, and the method comprises the steps: carrying out the feature dimension reduction processing of a hyperspectral image through principal component analysis; spatial spectrum collaborative information of hyperspectral data is deeply mined through a multi-scale spatial spectrum joint characterization module, and adaptive fusion and enhancement of spatial spectrum characteristics under different scales are realized; a dynamic context modeling strategy is introduced, and the perception ability of the model to context information is optimized by establishing a long-range dependency relationship between features; advanced feature integration and nonlinear transformation are carried out through a multi-layer perceptron, and precise classification of hyperspectral image ground objects is completed. According to the method, the performance superior to that of a current mainstream method is obtained on three public data sets, and the effectiveness and generalization ability of the method are verified.

The invention provides a hyperspectral image classification method and system based on spectral domain perception and uncertain regulation, and the method comprises the steps: building a hyperspectral image set, carrying out the noise removal of hyperspectral images, obtaining a first image, carrying out the normalization processing of the first image, obtaining a second image, obtaining the importance weight of each wave band of the second image based on a spectral domain perception mechanism, and carrying out the classification of the hyperspectral images. Projecting the feature tensor of the second image to obtain a projection feature tensor, performing dynamic weighted summation on the projection feature tensor based on importance weight in combination with an attention mechanism to obtain a dynamic focusing feature tensor, and performing nonlinear transformation and enhancement on the dynamic focusing feature tensor by using a multi-layer perceptron and performing training; and constructing an uncertainty evaluation unit dynamic regulation and control loss function, constructing an adaptive adjustment mechanism to adaptively adjust model parameters, and outputting a classification result by using the trained multi-layer perceptron. According to the method, the hyperspectral image with high dimension, complex characteristics, data imbalance and noise interference can be efficiently and accurately processed.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

According to the hyperspectral image classification method and system based on spectrum-space diffusion generation, a diffusion model is introduced to generate a hyperspectral generation sample, and a progressive denoising generation strategy of the diffusion model can generate a more diversified hyperspectral sample according with an actual spectrum physical rule while retaining spectrum details and a space structure; a category label embedding mechanism is introduced, a pre-trained diffusion model is finely adjusted, the generation capability of the diffusion model is optimized, and the problem of scarcity of small sample labeling is fundamentally relieved. Multiple screening standards including spectrum physical constraint, feature space alignment and semantic consistency verification are introduced, and authenticity and semantic consistency of generated samples in spectrum and space dimensions are ensured.

The invention discloses a hyperspectral image classification method based on frequency domain denoising and element gradient correction, and the method comprises the following steps: carrying out the preprocessing of all hyperspectral image data, and dividing an overall training sample set formed by the processed hyperspectral images into a training set and a verification set; constructing a sample weighting model based on frequency domain denoising and element gradient correction; in the training process, a parameterization frequency spectrum gating sensing transformation module is utilized to map features to a frequency domain through discrete Fourier transform, a learnable frequency spectrum response function is utilized to adaptively suppress spectrum jitter noise, and finally pure features are reconstructed. And automatically constructing a high-confidence pseudo-clean verification set based on a Gaussian mixture model and time domain consistency. According to the method, a time domain momentum updating mechanism is introduced, the variance of statistical estimation is effectively smoothed, random interference caused by training fluctuation is resisted, and the accuracy of pseudo clean set construction and the convergence stability of overall model training are further improved.

The invention relates to the technical field of hyperspectral image processing, in particular to a hyperspectral image classification model. The objective of the invention is to solve the problems of computation complexity and performance bottleneck in hyperspectral image processing. The model can be applied to hyperspectral image analysis tasks in the fields of remote sensing, environment monitoring, resource exploration and the like. Efficient dimension reduction processing is carried out by adopting random Fourier feature nonlinear principal component analysis (RFF-NLPCA), redundant information is effectively reduced, and the calculation speed is increased. A lightweight space SSM Block module and a spectrum SSM Block module are introduced into the model, and the image classification precision and robustness are improved through feature modeling of the space dimension and the spectrum dimension. The gating fusion module (GFM) optimizes the fusion capability of space and spectral features, and improves the discrimination capability of feature expression. Finally, the model can efficiently process high-dimensional hyperspectral data, provides a more accurate classification result, has relatively low calculation overhead and relatively high performance, and is particularly suitable for processing large-scale hyperspectral image data.

The invention provides a hyperspectral image classification enhancement method based on adaptive multi-scale superpixel constraint. The method comprises the following steps: establishing a main path and an auxiliary path; generating density information of an input feature map for the auxiliary path, and determining the number of superpixels; after the input feature map is compressed, smooth feature maps with different scales are generated; generating a corresponding number of superpixels in a corresponding kernel scale space according to the smooth feature map; for kernel scales, aggregating feature mean values in each superpixel region to obtain superpixel features of each kernel scale; calculating the weight of the corresponding kernel scale; obtaining a multi-scale super-pixel fusion feature map by using the weights and the aligned multi-scale features; total loss is constructed based on loss functions of the two classification paths; and training a neural network model of a main path and an auxiliary path by taking the neural network model as an optimization target to obtain an enhanced classification model for hyperspectral image classification. The method is compatible with an existing hyperspectral image classification network, and a lightweight enhancement scheme is provided for a deployed model.