Overcoming the Barrier of Incompleteness: A Hyperspectral Image Classification Full Model.

Abstract

Deep learning-based methods have shown promising outcomes in many fields. However, the performance gain is always limited to a large extent in classifying hyperspectral image (HSI). We discover that the reason behind this phenomenon lies in the incomplete classification of HSI, i.e., existing works only focus on a certain stage that contributes to the classification, while ignoring other equally or even more significant phases. To address the above issue, we creatively put forward three elements needed for complete classification: the extensive exploration of available features, adequate reuse of representative features, and differential fusion of multidomain features. To the best of our knowledge, these three elements are being established for the first time, providing a fresh perspective on designing HSI-tailored models. On this basis, an HSI classification full model (HSIC-FM) is proposed to overcome the barrier of incompleteness. Specifically, a recurrent transformer corresponding to Element 1 is presented to comprehensively extract short-term details and long-term semantics for local-to-global geographical representation. Afterward, a feature reuse strategy matching Element 2 is designed to sufficiently recycle valuable information aimed at refined classification using few annotations. Eventually, a discriminant optimization is formulized in accordance with Element 3 to distinctly integrate multidomain features for the purpose of constraining the contribution of different domains. Numerous experiments on four datasets at small-, medium-, and large-scale demonstrate that the proposed method outperforms the state-of-the-art (SOTA) methods, such as convolutional neural network (CNN)-, fully convolutional network (FCN)-, recurrent neural network (RNN)-, graph convolutional network (GCN)-, and transformer-based models (e.g., accuracy improvement of more than 9% with only five training samples per class). The code will be available soon at https://github.com/jqyang22/ HSIC-FM.