A Semi-Supervised Domain Alignment Transformer for Hyperspectral Images Change Detection

Supervised deep learning (DL)-based hyperspectral images change detection (HSIs-CD) has demonstrated excellent performance; however, current methods require many labeled training samples, and labeling the dataset is labor-intensive, limiting the application of high-precision supervised learning. Besides, there has been a lack of breakthroughs in unsupervised HSIs change detection (CD) methods due to the different feature distributions of bitemporal HSIs. Here, we propose a semi-supervised domain alignment transformer (DA-Former) for HSIs-CD to address the issues with limited samples. Specifically, a dual-branch transformer autoencoder (TAE) is designed, where the middle layer weights of the dual-branch transformer are shared, pulling features from different data into the same space. Moreover, the TAE is also trained cyclically to align the domains. Although the bitemporal HSIs features are cross domain, there is still confusion between the features of different objects. Thus, two fully connected (FC) layers are employed to classify the HSIs middle features extracted by the TAE into changed class or unchanged class with limited labeled data. Three HSIs-CD datasets are used to test this method, showing that TAE can align bitemporal HSIs domains and achieve the highest accuracy compared with benchmark approaches.