Spatial-Spectral ConvNeXt for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is a difficult task due to the heterogeneous spatial-spectral information, high-dimensionality, and noise effect in the HSI. Lately, an enhanced convolutional approach, i.e., ConvNeXt, has demonstrated a stronger feature representation capability than the popular vision transformer approaches. This article presents a spatial-spectral ConvNeXt approach, called SS-ConvNeXt, for hyperspectral classification. To better learn the spatial and spectral information in the HSI, the Spatial-ConvNeXt block, Spectral-ConvNeXt block, and spectral projection module are, respectively, designed. The depthwise and pointwise convolutions are adopted to reduce the model size and prevent vanishing gradient. The proposed model is evaluated against 14 other state-of-the-art methods on four different HSI datasets. Moreover, extensive ablation studies are conducted to investigate the roles of building blocks in the proposed model. The results demonstrate that the proposed method not only can achieve a high classification accuracy but also can better preserve class boundaries and reduce within-class noise.