Landslide extraction from aerial imagery considering context association characteristics

• Published in: International journal of applied earth observation and geoinformation 2024-07, Vol.131, p.103950, Article 103950
• Main Authors: Xie, Yakun, Zhan, Ni, Zhu, Jun, Xu, Bingli, Chen, Hongyu, Mao, Wenfei, Luo, Xiaojun, Hu, Ya
• Format: Article
• Language: English
• Subjects: Aerial imagery landslide dataset; Attention mechanism; Context association characteristics; Landslide extraction
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2024.103950

•Context information is fully considered to improve the accuracy of landslide extraction.•Two-branch network can improve the ability of feature extraction.•The fusion of encoder and decoder improves the anti-interference ability of the network.•The effect of deeply supervised classifier is better than multi-scale output. Accurate extraction of landslide information is crucial for timely disaster emergency response, yet this process faces significant challenges due to the interference of bare land and vegetation cover as well as the variability in landslide scales. Furthermore, the scarcity of comprehensive landslide datasets has hindered the in-depth exploration of deep learning techniques in this domain. Addressing these issues, this study introduces a novel framework for landslide extraction that leverages the characteristics of context association. In the encoding phase, a two-branch multiscale context feature extraction module (TMCFM) is established, which captures the contextual relationships across different scales through an attention mechanism while concurrently extracting context information within the same scale. To refine the interplay between features at different levels, self-attention is utilized, allowing for the effective fusion of context information at both ends of the same scale through feature fusion exploiting static and dynamic context feature modules (FSDC). Building upon this, this study develops a deeply supervised classifier (DSC) that enhances the network’s discriminative capabilities in the prediction phase via six auxiliary branches. Additionally, this study contributes a new aerial image dataset for landslide extraction created through meticulous visual interpretation. The proposed method is thoroughly compared with 17 contemporary deep learning methods, demonstrating an increase in the intersection-over-union (IoU) metric of 0.92%-16.94% over these methods. The robustness and superiority of the proposed approach are further validated through various discussions and analyses.