Landslide Inventory Mapping from Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation

Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of a postevent images, making use of spatial feature of landslide. Compared with some similar state-of-the-art methods, the proposed framework has three advantages: 1) the generation of LIM is almost automatic; 2) the framework can achieve more accurate results because it takes into account the landslide spatial information in an irregular manner through multisegmentation of the postevent image and object-based majority voting (MV); and 3) it needs less parameter tuning. The proposed framework was applied to four landslide sites on Lantau Island, Hong Kong. Compared with existing methods, including region level set evolution (RLSE), edge level set evolution (ELSE) and change detection Markov random field (CDMRF) methods, quantitative evaluation shows the proposed framework is competitive in terms of Completeness. The framework outperformed RLSE, ELSE, and CDMRF for the four experiments by more than 9% in Correctness and by 8% in Quality. To the authors' knowledge, this is the first-time that landslide spatial information has been utilized through the integration of multiscale segmentation of postevent image with the MV approach to obtain LIM using high spatial resolution remote sensing images. The approach is also of wide generality and applicable to other kinds of land cover change detection using remote sensing images.