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

Multi-temporal 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 bi-temporal HSIs is labor-intensive. In this paper, 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 multi-scale mask change simulation strategy (MMCS) 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 multi-temporal 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.