Computationally Lightweight Hyperspectral Image Classification Using a Multiscale Depthwise Convolutional Network With Channel Attention

Abstract

Convolutional networks have been widely used for the classification of hyperspectral images; however, such networks are notorious for their large number of trainable parameters and high computational complexity. Additionally, traditional convolution-based methods are typically implemented as a simple cascade of a number of convolutions using a single-scale convolution kernel. In contrast, a lightweight multiscale convolutional network is proposed, capitalizing on feature extraction at multiple scales in parallel branches followed by feature fusion. In this approach, 2D depthwise convolution is used instead of conventional convolution in order to reduce network complexity without sacrificing classification accuracy. Furthermore, multiscale channel attention is also employed to selectively exploit discriminative capability across various channels. To do so, multiple 1D convolutions with varying kernel sizes provide channel attention at multiple scales, again with the goal of minimizing network complexity. Experimental results reveal that the proposed network not only outperforms other competing lightweight classifiers in terms of classification accuracy but also exhibits a lower number of parameters as well as significantly less computational cost.