STMNet: Single-Temporal Mask-Based Network for Self-Supervised Hyperspectral Change Detection

Multitemporal hyperspectral images (HSIs) have been widely applied in change detection (CD) of different land covers for their rich spectral features and image details. However, alignment and labeling pairs of bitemporal HSIs are labor-intensive. In this article, we propose a single-temporal mask-based network (STMNet) for self-supervised HSI CD from a new perspective of detecting masks as changes. STMNet implements self-supervised by treating artificially constructed masks attached to single-temporal HSI as changed regions. To this end, we design a multiscale mask change simulation (MMCS) strategy to generate pseudo-second-temporal HSI closer to the real case. Meanwhile, a global-local feature aggregation network is proposed to enhance long-distance and local spatial-spectral feature extraction. To the best of our knowledge, this is the first work in the field of HSI CD that uses single-temporal HSIs and eliminates the need for labeling and pairing samples, alleviating the problem of difficult multitemporal HSI annotation. The visual and quantitative experimental results on three HSI datasets show that the proposed STMNet outperforms the compared state-of-the-art methods for HSI CD. Codes are available at https://github.com/Zhoutya/ChangeDetection-STMNet.