A Lightweight Spectral-Spatial Convolution Module for Hyperspectral Image Classification

Abstract

Convolutional neural networks (CNNs) showed impressive performance for hyperspectral image (HSI) classification. Nevertheless, convolutional layers contain massive parameters, which restrict the deployment of CNNs on satellite and airborne platforms with limited storage and computing resources. In this letter, we propose a lightweight spectral-spatial convolution module (LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM) as an alternative to the convolutional layer. The proposed LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM can greatly reduce network parameters and computational complexity in terms of multiply-accumulate operations (MACs) while maintaining or even improving the classification performance. Furthermore, it is a plug-and-play component and can be used to upgrade existing CNN-based models for HSI classification. Experimental results on two benchmark HSI data sets demonstrate that the proposed LS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CM achieves competitive results in comparison with other state-of-the-art methods.