Landslide Mapping Using Multilevel-Feature-Enhancement Change Detection Network

Abstract

Landslide mapping (LM) from bitemporal remote sensing images is essential for disaster prevention and mitigation. Although bitemporal change detection technology has been applied for LM, there remains room for improvement in its accuracy and automation. In this article, a multilevel feature enhancement network (MFENet) is proposed for LM based on modules built in convolutional neural networks (CNNs) like CNN-Attention. MFENet mainly consists of three modules: the postevent feature enhancement module (PFEM), the bifeature difference enhancement module (BFDEM), and the flow direction calibration module (FDCM). Specifically, the main role of PFEM is to selectively fuse postevent multilayer features to provide discriminative postevent features. BFDEM fuses the multilayer differences of both pre-event and postevent features to generate high-quality change detection features, which are sufficiently powerful to distinguish foreground from background. FDCM uses a digital elevation model to calibrate the flow direction of each pixel of the landslide detection results to complete the LM task. Experiments were conducted to test the effectiveness of MFENet on two real-world regions, Lantau Island and Sharp Peak, Hong Kong, where landslides occurred after rainstorms. Compared with other state-of-the-art general change detection methods and landslide-specific change detection methods, the proposed method outperforms all metrics, with its intersection over union reaching 87.23%. The availability of additional features and the generalization performance of MFENet are demonstrated experimentally. It is anticipated that the proposed network will further contribute to disaster prevention and mitigation.

Keywords

• Change detection
• convolutional neural network (CNN)
• flow direction
• landslide mapping (LM)
• remote sensing images