Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification

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Over the past few years, hyperspectral image classification using convolutional neural networks (CNNs) has progressed significantly. In spite of their effectiveness, given that hyperspectral images are of high dimensionality, CNNs can be hindered by their modeling of all spectral bands with the same weight, as probably not all bands are equally informative and predictive. Moreover, the usage of useless spectral bands in CNNs may even introduce noises and weaken the performance of networks. For the sake of boosting the representational capacity of CNNs for spectral-spatial hyperspectral data classification, in this work, we improve networks by discriminating the significance of different spectral bands. We design a network unit, which is termed as the spectral attention module, that makes use of a gating mechanism to adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. We theoretically analyze and discuss why such a spectral attention module helps in a CNN for hyperspectral image classification. We demonstrate using extensive experiments that in comparison with state-of-the-art approaches, the spectral attention module-based convolutional networks are able to offer competitive results. Furthermore, this work sheds light on how a CNN interacts with spectral bands for the purpose of classification.