Self-Supervised Convolutional Neural Network via Spectral Attention Module for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is a hot topic in the field of remote sensing, and convolutional neural networks (CNNs) have shown good classification performance because of their capabilities of feature extraction. However, traditional CNN-based methods require a lot of labeled data during their training process, although the acquisition of labeled samples is complicated and time-consuming. In addition, a key issue for HSI classification is how to effectively explore the correlation within the spectral dimension and emphasize important spectral bands. In this letter, an end-to-end framework named spectral attention-based self-supervised CNN (SASCNN) is put forward for HSI classification. At first, the SASCNN takes raw 3-D cubes as input data, and a spectral attention module (SAM) is used to adaptively optimize channel-wise characteristics by adjusting the importance among continuous spectral bands. Then, by flexibly adding multilayer concatenation to integrate shallow and abstract features, the designed encoder–decoder part can be used to learn discriminative features and reproduce the inputs in a self-supervised manner. Experiments over the Heihe and Houston datasets demonstrate the effectiveness of the proposed self-supervised learning method.