Unsupervised Landslide Detection From Multitemporal High-Resolution Images Based on Progressive Label Upgradation and Cross-Temporal Style Adaption

Multitemporal landslide inventory mapping plays a vital role in postdisaster reconstruction, landslide prevention, and regional ecosystem restoration. While deep learning methods have achieved great success in landslide detection tasks, previous landslide detection approaches hardly use unlabeled samples to optimize models to distinguish landslide changes in multitemporal applications due to insufficient labeled data across different periods. To address this issue, we propose a novel method called progressive label upgradation and cross-temporal style adaption (PluTsa) for unsupervised multitemporal landslide detection. At the interdomain level, we introduce a paired image-to-image cross-temporal domain style adaption strategy to reduce visual differences among multitemporal remote sensing images. Besides, a temporal-aware pairing constraint (tpc) strategy is designed to further mitigate uneven feature distribution problems and align domain features. At the intradomain level, we propose a novel progressive label upgradation (PLU) scheme to produce high-quality pseudolabels that guide the deep learning model to extract valuable landslide features by connecting the geographic locations of cross-temporal images. The proposed method is evaluated on two datasets, and extensive experimental results demonstrate that PluTsa significantly outperforms other state-of-the-art methods, indicating it has promising prospects in unsupervised landslide detection from multitemporal high-resolution images.