ROBYOL: Random-Occlusion-Based BYOL for Hyperspectral Image Classification
Jinhui Li, Xiaorun Li, Zeyu Cao, Liaoying Zhao
Abstract
With the development of deep learning, hyperspectral image classification (HSIC) has improved rapidly in recent years. Unsupervised feature learning algorithms play an important role in extracting features from hyperspectral images (HSIs). This letter proposed a random-occlusion-based Bootstrap-Your-Own-Latent network (ROBYOL), combining a new augmentation method and a superior contrastive learning algorithm for feature extraction. The proposed method consists of a self-supervised learning part for feature extracting and a classifier part as the downstream task. It can be proved by the experimental results that the feature extraction ability of the network is effective in this way. Furthermore, the influence of different occlusion strategies is also studied, including changing occlusion area and occlusion value, and we proposed translucent occlusion. Our results with two well-known HSIs reveal that proper occlusion strategies can improve hyperspectral classification results effectively.