Abstract
Recently, hyperspectral image (HSI) classification has attracted increasing attention in the remote sensing field. Plenty of CNN-based methods with diverse attention mechanisms (AMs) have been proposed for HSI classification due to AMs being able to improve the quality of feature representations. However, some of the previous AMs squeeze global spatial or channel information directly by pooling operations to yield feature descriptors, which inadequately utilize global contextual information. Besides, some AMs cannot exploit the interactions among channels or positions with the aid of nonlinear transformation well. In this article, a spectral-spatial network with channel and position global context (GC) attention (SSGCA) is proposed to capture discriminative spectral and spatial features. Firstly, a spectral-spatial network is designed to extract spectral and spatial features. Secondly, two novel GC attentions are proposed to optimize the spectral and spatial features respectively for feature enhancement. The channel GC attention is used to capture channel dependencies to emphasize informative features while the position GC attention focuses on position dependencies. Both GC attentions aggregate global contextual features of positions or channels adequately, following a nonlinear transformation. Experimental results on several public HSI datasets demonstrate that the spectral-spatial network with GC attentions outperforms other related methods.
Highlights
Compared with traditional panchromatic and multispectral remote sensing images, hyperspectral images (HSIs) contain rich spectral information owing to the hundreds of narrow contiguous wavelength bands
In order to alleviate the problems of attention mechanisms (AMs) in the double-branch multi-attention mechanism network (DBMA) and the double-branch dual-attention mechanism network (DBDA), we developed two novel AMs known as channel global context (GC) attention and position GC
When 5% of the samples are randomly selected for training on the Indian Pines (IN) dataset, our method achieves the best accuracy with 98.13% overall accuracy (OA), improving 1.14% over the DBDA
Summary
Compared with traditional panchromatic and multispectral remote sensing images, hyperspectral images (HSIs) contain rich spectral information owing to the hundreds of narrow contiguous wavelength bands. To take advantage of abundant spectral information, traditional HSI classification methods tend to take an original pixel vector as the input, such as κ-nearest neighbors (KNNs) [9], multinomial logistic regression (MLR) [10], and linear discriminant analysis (LDA) [11] These methods mainly focus on two steps: feature engineering and classifier training. An end-to-end spectral-spatial framework with channel and position global context (GC) attention (SSGCA) is proposed for HSI classification. The channel GC attention is designed to capture interactions among channels, while the position GC attention is invented to explore interactions among positions Both GC attentions can make full use of global contextual information with less time consumption and model long-range dependencies to obtain more powerful feature representations.
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