CDFormer: A Hyperspectral Image Change Detection Method Based on Transformer Encoders

Abstract

Hyperspectral image (HSI) change detection (CD) has gained much attention in remote sensing. However, most deep learning methods are restricted by a limited receptive field, without leveraging temporal information, and the need for many training samples. In this letter, we proposed a Transformer Encoder-based HSI CD framework called CDFormer. First, space and time encodings are added to the pixel sequence to guide transformers to exploit change information of space and time by the pixel embedding (PE) module. Second, the self-attention component of Transformer Encoder module has a global space-time receptive field to mine the correlation and interaction between bi-temporal features, enhancing the utilization of temporal dependencies. Next, the multi-head attention mechanism learns several attentions and extracts the joint weighted spatial-spectral-temporal features, which improves the feature discrimination ability of the changes. Finally, the detection result is predicted using a fully connected network. It is notable to mention that the proposed method only uses a few labeled samples to train the network. Experiments on two HSI datasets demonstrate that our proposed method can get effective performance in HSI CD.