Deep Multiple Instance Learning for Landslide Mapping
Min Zhang, Wenzhong Shi, Shanxiong Chen, Zhao Zhan, Zhi‐Cheng Shi
Abstract
In this letter, a novel neural network (CDMI-Net) that combines change detection and multiple instance learning (MIL) is proposed for landslide mapping. After obtaining a score map of landslides provided by the network, the final binary map is generated by fast postprocessing. The benefits of the proposed method are threefold. First, using the MIL framework, the network is trained only by the scene-level samples and it reduces the need for pixel-level samples. Second, a change-detection network architecture using a two-stream U-Net with shared weights is designed to learn the deep features of the landslide from the two-period aerial images, reducing the false-positive results. Third, integrating a gated attention-based pooling layer and a fast level-set evolution algorithm can finally produce the pixel-level results. Experimental results show that the proposed CDMI-Net achieves comparable and even better performance on the testing image pairs than all other methods and has great potential for the landslide mapping application.