Extraction of Scattered Small-Scale Landslides Distribution by Object-Based Classification Using Optical High-Resolution Satellite Images

Abstract

Abstract. Continuous heavy rain for a long duration over mountainous terrain, where the elevation is relatively low and the topography is complex leads to multiple small-scale landslides over a wide area. Detailed investigations of small-scale landslides have been effectively carried out using optical high-resolution satellite images with spatial resolution of about 2 m or less. In this study, the sediment-related disaster caused by heavy rain in northern Kyushu, Japan that occurred in July 2017 was selected as a typical example of small-scale landslide. For this landslide event, the applicability of the conventional superpixel segmentation for landslide separation was examined. The applicability of the representative SLIC and SLICO methods in the superpixel segmentation method by image interpretation in the case of a large number of small-scale landslides in a wide area was assessed. These results suggest that in the case of such a disaster, segmentation by the SLICO method will be better. In addition, the set value of the area size for the area division was systematically examined from the distribution tendency of the average NDVI value in the divided area. It was shown that the landslide region can be extracted with relatively high accuracy by the land cover classification process by the NN method by using the appropriate region size examined by the SLICO method.

A Novel Method for Extracting Time Series Information of Deformation Area of a Single Landslide Based on Improved U-Net Neural Network

“…Zhu et al, proposed a region merging method (Zhu et al, 2016). Hashiba et al, uses the superpixel SLICO method to check the appropriate area size to extract the landslide area with high accuracy (Hashiba and Sonobe, 2020). Yang et al, used superpixel algorithm to realize the automatic extraction of landslide deformation information (Yang et al, 2019).…”