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Graph convolution networks (GCNs) have been applied in a variety of fields due to their powerful ability in processing graph-like data. However, the massive number of hyperspectral pixels makes it challenging to define general graph structures on hyperspectral images (HSIs). On the other hand, convolutional neural networks (CNNs) take in regular image regions with fixed square size, and have demonstrated impressive accuracy while being efficient in computation. Inspired by the classification framework of CNNs, we develop a GCN-based model that generates effective local spectral–spatial features for HSI classification. Specifically, graph convolutions are performed separately on every local region, which significantly limits the graph’s size. While graph convolution extracts features of every pixel, it does not reduce the number of them. To fuse suitable representations for the classification task, we develop a graph pooling operation to preserve classification-specific features and reduce redundant pixels. Based on local regions of HSIs, pooling in the graph domain is equivalent to spatial pooling in the spatial domain. The proposed method is thus named the spatial pooling graph convolutional network (SPGCN). Experimental results on several typical datasets demonstrated that the proposed SPGCN provides competitive results compared with other state-of-the-art CNN-based methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residual network (SPRN) is proposed for HSI classification. The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral–spatial features from these subbands, respectively. Finally, the features are fused and fed into a classifier. By equivalently using grouped convolutions, the spectral partition and feature extraction are embedded into an end-to-end network. Experimental results show that the proposed SPRN achieves state-of-the-art performance, meanwhile, with relatively fewer parameters and computational costs. Usually, the CNN takes a patch that contains continuous spatial information as the input and results in a class label of the center pixel. The large size of the input patch includes more spatial information, whereas also introduces interfering pixels that may lead to a degradation of classification accuracies. For that reason, we propose a novel spatial attention module named homogeneous pixel detection module (HPDM). The module alleviates the degradation of performance as the input patch size increases by capturing the homogeneous pixels in the input patch. The module can be integrated into any CNN-based HSI classification framework. [ABSTRACT FROM AUTHOR]