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

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.