Graph convolutional networks (GCNs) have garnered extensive attention in the realm of hyperspectral image (HSI) classification. However, due to the problem of over-smoothing caused by deep GCN, most of the existing GCN-based methods are limited to constructing shallow networks, thus only able to extract superficial features. Moreover, when existing shallow GCNs extend to a more deeper structure, the number of learnable parameters increase linearly, thus leading to poor generalization performance under limited training samples. To address the aforementioned issues, a Separable Deep Graph Convolutional Network Integrated with CNN and Prototype Learning (SDGCP) is proposed for HSI classification, which can extract effective global structural information of HSI without increasing the number of trainable parameters. Specifically, the spectral and spatial features, adaptively selected by the attention module, are encoded into the structure of a graph by the graph encoder with the assistance of the pixel-to-region mapping obtained from the simple linear iterative clustering (SLIC). Then, a separable deep graph convolution module, composed of feature extraction and deep feature propagation, is adopted to capture the long-range contextual relationships from HSI encoded as graph data, which is combined with locally complementary information extracted by CNN after decoding. Finally, to further boost the performance of classification under limited labeled samples, prototype learning with regularization terms is utilized to enhance the intra-class compactness and inter-class separability of feature representations. Extensive experiments on three standard HSI data sets demonstrate the superiority of the proposed SDGCP over the state-of-the-art (SOTA) methods.

Transformers can model global context to enhance the performance of hyperspectral classification. However, the explored global information is generally confined to the spatial neighborhood of target pixels. In order to fully leverage global correlation across broader areas, a deformable graph transformer (DGT) is proposed for hyperspectral classification, in which the global information within an entire image is explored to improve the classification performance. Specifically, DGT layers are designed to adaptively sample virtual nodes at varying distances from an initial graph constructed from an image, by which the global spatial information can be explored using a deformable graph self-attention (DGSA) mechanism. Moreover, a learnable absolute position encoding (LAPE) module is constructed to enhance the spatial context awareness of DGT by integrating positional information into the graph nodes. In addition, graph structure encoding and graph topology encoding are further designed as inductive biases for the graph, by which both local structural information and global topological information of the HSI are captured to enhance the feature extraction capability of the DGT layer. Ultimately, through the stacking of multiple DGT layers, a composite feature fusion learning (CFFL) module is employed to fully utilize the simple low-level and complex abstract high-level features extracted from different layers. Extensive experiments on four datasets demonstrate the superiority and robustness of the proposed DGT over several state-of-the-art (SOTA) methods in terms of various evaluation criteria.

Learning discriminative features is of crucial for hyperspectral image (HSI) classification. Though metric learning has been applied to learn effective features in HSI classification tasks, existing metric loss functions only consider distance among features of sample pairs but ignore the feature centers and boundaries in the embedding feature space, which limits the discrimination of learned features. In this paper, a novel metric loss function named center-boundary metric loss (CBML) is proposed to learn more discriminative features so as to improve HSI classification performance. Unlike the existing metric loss functions, CBML not only considers the distance between sample pairs to enhance intra-class similarity and inter-class separability but also pays more attention to the feature centers and boundaries in the embedding feature space that could greatly determine and affect the category of features. Specifically, CBML forces the distance of a sample to its corresponding feature center to be explicitly smaller than that to samples from other classes by a predefined threshold. As a result, the boundaries of different classes will separate an actual distance, which improves the discrimination of learned features. Moreover, in order to improve the training efficiency, a cross mini-batch sampling strategy is further proposed to break through the limitation within the mini-batch by using features between several contiguous mini-batches to sample pairs without increasing the size of the mini-batch. Accordingly, the sampling range of sample pairs is greatly expanded, and the training data is more fully exploited. Experimental results over four benchmark datasets with a typical network for HSI classification demonstrate our proposed method outperforms several state-of-the-arts.

Feature representation is crucial for hyperspectral image (HSI) classification. However, existing convolutional neural network (CNN)-based methods are limited by the convolution kernel and only focus on local features, which causes it to ignore the global properties of HSIs. Transformer-based networks can make up for the limitations of CNNs because they emphasize the global features of HSIs. How to combine the advantages of these two networks in feature extraction is of great importance in improving classification accuracy. Therefore, a cross-attention fusion network bridging CNN and Transformer (CAF-Former) is proposed, which can fully utilize the advantages of CNN in local features and Transformer’s long time-dependent feature learning for hyperspectral classification. In order to fully explore the local and global information within an HSI, a Dynamic-CNN branch is proposed to effectively encode local features of pixels, while a Gaussian Transformer branch is constructed to accurately model the global features and long-range dependencies. Moreover, in order to fully interact with local and global features, a cross-attention fusion (CAF) module is proposed as a bridge to fuse the features extracted by the two branches. Experiments over several benchmark datasets demonstrate that the proposed CAF-Former significantly outperforms both CNN-based and Transformer-based state-of-the-art networks for HSI classification.

Transformer-based networks, which can well model the global characteristics of inputted data using the attention mechanism, have been widely applied to hyperspectral image (HSI) classification and achieved promising results. However, the existing networks fail to explore complex local land cover structures in different scales of shapes in hyperspectral remote sensing images. Therefore, a novel network named multiscale and cross-level attention learning (MCAL) network is proposed to fully explore both the global and local multiscale features of pixels for classification. To encounter local spatial context of pixels in the transformer, a multiscale feature extraction (MSFE) module is constructed and implemented into the transformer-based networks. Moreover, a cross-level feature fusion (CLFF) module is proposed to adaptively fuse features from the hierarchical structure of MSFEs using the attention mechanism. Finally, the spectral attention module (SAM) is implemented prior to the hierarchical structure of MSFEs, by which both the spatial context and spectral information are jointly emphasized for hyperspectral classification. Experiments over several benchmark datasets demonstrate that the proposed MCAL obviously outperforms both the convolutional neural network (CNN)-based and transformer-based state-of-the-art networks for hyperspectral classification.

In recent years, convolutional neural networks (CNNs) have been successfully applied in hyperspectral image (HSI) classification tasks. However, the spatial-spectral features within an HSI have not been well explored using convolutions in CNNs. In the paper, a novel end-to-end hierarchical spatial-spectral transformer (HSST) is proposed for HSI classification, in which effective spatial-spectral features are emphasized using multi-head self-attention mechanism (MHSA). MHSA module captures better internal correlation of HSI data than the traditional convolution operation and can compute weighting scores for spatial and spectral context of pixels. Furthermore, a hierarchical architecture is designed to reduce a large number of parameters in the original transformer-style networks while still achieving satisfying classification results. Experimental results over two benchmark HSI datasets demonstrated the proposed HSST obviously outperforms several state-of-the-art deep learning-based HSI classification algorithms.

Hyperspectral image (HSI) classification is a critical task with numerous applications in the field of remote sensing. Although convolutional neural networks have achieved remarkable success in computer vision, they are still limited in the ability to model long-term dependencies due to small receptive fields. Recently, vision transformers have been used in HSI classification, where multi-head self-attention (MHSA), as the key feature extractor of transformers, learns global dependencies in long-range positions and bands of HSI pixels. Existing vision transformers for classifying HSIs with a large number of bands, however, have some limitations in that features extracted by MHSA may exhibit over-dispersion. In this article, we propose a Group-Aware Hierarchical Transformer (GAHT) for HSI classification, which confines MHSA to the local spatial–spectral context by introducing a new grouped pixel embedding (GPE) module. The GPE emphasizes local relationships within HSI spectral channels, resulting in a global–local fashion from a spatial–spectral context for HSI classification. In addition, we construct our transformer in a hierarchical manner, which can significantly improve classification accuracy with only a few parameters. Extensive experiments on four benchmark HSI datasets demonstrate that the proposed method outperforms state-of-the-art HSI classification algorithms. The source code is available athttps://github.com/MeiShaohui/Group-Aware-Hierarchical-Transformer.