Do We Need Learnable Classifiers? A Hyperspectral Image Classification Algorithm Based on Attention-Enhanced ResBlock-in-ResBlock and ETF Classifier

Abstract

Hyperspectral image classification plays an important role in the field of remote sensing. Even though we can easily acquire hyperspectral remote sensing images, obtaining a large number of labeled hyperspectral samples remains challenging, especially in high-altitude or uninhabited areas. In this paper, we propose a hyperspectral classification scheme for scenarios with insufficient labeled samples. This scheme is based on a variant of the ResBlock and a non-learned classifier. First, we introduce a new and simplified backbone network for feature extraction. This network primarily consists of an attention-enhanced ResBlock-in-ResBlock module, which utilizes nested residual modules to enhance nonlinear expression and further optimizes the network using channel attention. Building upon this foundation, we address the challenge of achieving optimal classification with limited labeled training samples, a scenario described by the neural collapse theory. To address this, we introduce the Equiangular Tight Frame (ETF) classifier and the dot-regression loss into hyperspectral classification. We conducted extensive comparative experiments using three hyperspectral image datasets. The experimental results demonstrate that our algorithm achieves superior classification accuracy, especially when the training sample size is small, outperforming other state-of-the-art algorithms. Furthermore, our algorithm maintains a low number of parameters and an overall complexity level.