Landslide Detection for Remote Sensing Images Using a Multilabel Classification Network Based on Bijie Landslide Dataset

Abstract

To effectively mitigate disaster damage, it is crucial to obtain landslide information quickly and accurately with the abundant remote sensing images. Although related landslide detection research has been carried out a lot in recent years, all of the research articles are still based on single-label image classification. Meanwhile, the existing methods face challenges in balancing global and local information effectively, interpreting and modeling intricate interlabel dependencies. To solve the problems above, we perform multilabel annotation on the Bijie landslide dataset and propose a novel multilabel classification network for landslide detection. The proposed network consists of a feature representation module and a category relation parsing module. The first module is designed to extract feature maps that represent high-level semantic information and convert them into feature sequences. This can fully utilize the capability of the bidirectional long short-term memory network to model label dependencies. The second module is used to model interlabel dependencies to enhance the ability of the model for efficiently detecting landslides. Compared with other recent landslide detecting methods, the results show that the proposed scheme is more effective. F1 score on landslide detection reaches up to 97.56% and the attention area of categories is more precise in the proposed method. Therefore, the proposed model can provide reliable and effective decision support for disaster emergency response.

Keywords

• Bidirectional long short-term memory (BiLSTM)
• landslide detection
• multilabel classification (MLC)
• remote sensing images
• Swin transformer