A Multi-Channel Convolutional Neural Network Model for Detecting Active Landslides Using Multi-Source Fusion Images
Year: 2025
Type: article
Abstract: Synthetic Aperture Radar Interferometry (InSAR) has demonstrated significant advantages in detecting active landslides. The proliferation of computing technology has enabled the combination of InSAR and deep learning, offering an innovative approach to the automation of landslide detection. However, InSAR-based detection faces two persistent challenges: (1) the difficulty in distinguishing active landslides from other deformation phenomena, which leads to high false alarm rates; and (2) insufficient accuracy in delineating precise landslide boundaries due to low image contrast. The incorporation of multi-source data and multi-branch feature extraction networks can alleviate this issue, yet it inevitably increases computational cost and model complexity. To address these issues, this study first constructs a multi-source fusion image dataset combining optical remote sensing imagery, DEM-derived slope information, and InSAR deformation data. Subsequently, it proposes a multi-channel instance segmentation framework named MCLD R-CNN (Multi-Channel Landslide Detection R-CNN). The proposed network is designed to accept multi-channel inputs and integrates a landslide-focused attention mechanism, which enhances the model’s ability to capture landslide-specific features. The experimental findings indicate that the proposed strategy effectively addresses the aforementioned challenges. Moreover, the proposed MCLD R-CNN achieves superior detection accuracy and generalization ability compared to other benchmark models.
Source: Remote Sensing
DOI: 10.3390/rs18010126
Authors: Jun Wang, Hongdong Fan, Wanbing Tuo, Yiru Ren
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Award: G6214180789
OpenAlex ID: W7117659809