A Simple But Effective Landslide Detection Method Based on Image Saliency

Abstract

Effective large-scale landslide mapping is becoming significantly important for analyzing natural hazards and providing landslide locations rapidly for emergency response. Change detection and machine learning methods are commonly used for landslide detection. Change detection mostly relies on several experienced parameters that users have to tune for different images, which limits the practical application. The training machine learning model consumes much time, and it is limited to specific imaging conditions. In this paper, a simple method for landslide detection using a fixed parameter by calculating image saliency is proposed. Landslide is detected as a saliency object within the background of vegetation and bare rocks. It is fast and robust for the experimental images, and outperforms the state-of-the-art, semi-automatic method in terms of accuracy and computing time. Given the high efficiency and robustness of the proposed method, it is applicable to practical cases for hazard estimation.