Center Mask Self-Attention Network for Hyperspectral Image Classification

Benefiting from the thousands of continuous band information in hyperspectral images (HSIs), the task of HSI classification has become an indispensable part of the field of remote sensing. With the development of deep learning, deep learning techniques such as convolutional neural networks have been widely introduced into HSI classification research. However, most of these methods do not fully consider the potential relationship between the central pixel and surrounding neighborhoods. Therefore, we introduce a novel center mask self-attention network (CMSAN) to enable the model to effectively capture the association between the central pixel and its neighbors for better feature extraction. We conduct experiments on two publicly available HSI datasets. The positive results on both datasets fully demonstrate the effectiveness of our proposed method.