MB-Net: A network for accurately identifying creeping landslides from wrapped interferograms

作者：Ruixuan Zhang, Wu Zhu, Baodi Fan, Qian He, Jiewei Zhan, Chisheng Wang, Bochen Zhang · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.104300 · 被引用次数：10 · 研究领域：Landslides and related hazards、Synthetic Aperture Radar (SAR) Applications and Techniques、Remote Sensing and LiDAR Applications

• An intelligent landslide identification method using wrapped interferograms and CNNs. • A network featuring parallel branch encoding and progressive feature fusion. • The method achieves high spatial transferability across various public data sources. The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient ...