HSI-Mixer: Hyperspectral Image Classification Using the Spectral–Spatial Mixer Representation From Convolutions
Hongbo Liang, Wenxing Bao, Xiangfei Shen, Xiaowu Zhang
Abstract
Transformer networks have shown impressive performance for hyperspectral interpretation. Nevertheless, the high-dimensional redundant spectral distribution of hyperspectral images (HSIs) hinders their validity of interaction between features from distant locations. In this letter, we propose the HSI-Mixer, a novel extremely simple convolution neural network (CNN), which is similar in spirit to Transformer to re-consider the remarkable inductive biases of convolutions. In specific, we construct a hybrid measurement-based linear projection (HMLP) to merge spectral signatures and spatial positions of an HSI cuboid. Meanwhile, according to the merging relations between spectral-spatial attributes, we establish both spectral and spatial Mixer blocks to separate features from a mixed volume to a pure one, across either spectral bands or spatial locations, respectively. Furthermore, our HSI-Mixer maintains the same-depth-and-resolution throughout the network. Experimental results on three benchmark datasets demonstrate that our proposal achieves promising performance, in contrast to other state-of-the-art methods. The codes of this work will be available at https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer.