ESSINet: Efficient Spatial-Spectral Interaction Network for Hyperspectral Image Classification

Nowadays, convolutional neural networks (CNNs) are widely used in the field of hyperspectral image (HSI) classification. However, a major feature of HSIs is their rich spectral-spatial information with hundreds of continuous bands. This inevitably incurs the problems of high computational cost for network optimization and high interredundancy in the convolution kernels. To solve these problems, in this article, we rethink HSIs from the spectral perspective and introduce a lightweight operator called involution, which can effectively solve the above limitations. Different from traditional convolution kernels, the involution kernels pay more attention to the features of the channels but usually ignore the spatial features in the receptive field. To incorporate both spatial and spectral information, we construct a dual-pooling layer and design a novel involution-2D operator and its more lightweight version, involution-1D operator. Finally, an efficient spatial-spectral interaction network (ESSINet) for HSI classification is proposed based on these two new operators, which can make the spatial-spectral information in HSIs interact more closely. Extensive experimental results on four public datasets demonstrate the effectiveness and efficiency of the proposed ESSINet over some state-of-the-art CNN-based networks.