A hybrid automatic thresholding approach using panchromatic imagery for rapid mapping of landslides

In this paper, we propose a hybrid approach for the rapid mapping of landslides, based on automatic thresholding using panchromatic (PAN) images as data sources. The new approach is a combination of image differencing, a multistage local thresholding strategy, and a newly developed directional recursive method. Firstly, the Otsu method has been selected to calculate the global threshold of the entire residual image and the image is repeatedly segmented into sub-images with a growing window size. Secondly, the resulting sub-images are categorized into three types (balanced, background-dominated, and object-dominated) according to the histogram of global and local image information. Thirdly, sub-images of different types are binarized respectively by using different schemes of the proposed directional recursive method. Finally, the individual results in each running step are integrated and the entire image is binarized. The proposed approach has been tested for rapid detection of landslides induced by the Wenchuan earthquake in Beichuan County, China. Results show that almost all of the landslides are properly detected including the low-contrast ones, which cannot be identified neither by the original Otsu method nor the change detection algorithm within ERDAS software.