S2WaveNet: A novel spectral–spatial wave network for hyperspectral image classification

Abstract

Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient representation of spectral–spatial information in HSI and generally aggregate features with fixed weights, which limits their ability to capture semantic differences. To tackle these challenges, this paper proposes a novel spectral–spatial wave network (S2WaveNet) for HSI classification tasks to enhance the representation capability of spectral–spatial features in ground objects. Specifically, the spectral–spatial wave mixer (S2WaveMixer) block is designed as a key component to represent each HSI input as a wave function with amplitude and phase parts. Thus, it enables a deeper dynamic perception and facilitates the extraction of spectral–spatial feature variations of ground objects. The amplitude represents the original features and the phase term is a complex value changing based on the semantic contents of the input images. Furthermore, the inception unit is introduced into the S2WaveMixer block to consider spectral–spatial information at multiple granularity levels. Experiments conducted on five public datasets demonstrate the superiority of S2WaveNet in classification performance and generalization compared to competitors.

Keywords

• Hyperspectral image classification
• Multi-layer perceptron (MLP)
• Wave representation
• Dynamic feature aggregation
• Inception unit