A Cross-Scene Few-Shot Learning Based on Intra–Inter Domain Contrastive Alignment for Hyperspectral Image Change Detection
Wenhui Hou, Jiangtao Peng, Bing Yang, Lanxin Wu, Weiwei Sun
Abstract
Recently, deep neural networks have demonstrated outstanding performance in hyperspectral image (HSI) change detection (CD), especially when there is sufficient labeled samples. However, the labels of HSI are difficult to obtain, and acquiring enough labels to train deep network is a great challenge in practice. Therefore, to mitigate the effect of insufficient labels in detection results, this paper proposes a cross-scene few-shot learning (FSL) network based on intra-inter domain contrastive alignment (CAFSL) for HSI-CD, which combines contrastive learning (CL) and FSL into a unified framework, aiming to achieve better detection results using only a few labeled samples. Specifically, we perform cross-scene FSL using a pair of dual-phase images from a very high-resolution image (VHRI) as the source domain and a pair of HSI as the target domain. Then, an intra-domain supervised contrastive learning (INSCL) module is designed to enhance the compactness within classes and widen the discrimination between classes by maximizing the feature similarity of intra-class and minimizing the feature similarity of inter-class. Finally, a cross-domain contrastive alignment (CRCA) module is proposed to align the features of source and target domains, which mitigates the effect of domain migration problems caused by different data types. Experiments on three HSI benchmark datasets reveal that the CAFSL algorithm outperforms current advanced algorithms based on deep learning and FSL while with limited labeled samples.