Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Abstract

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity. Therefore, building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides. Aiming at addressing this problem, a landslide interpretation model of high-resolution images based on bag of visual word (BoVW) feature was proposed. The high-resolution images were pre-processed, and then BoVW feature and support vector machine (SVM) was adopted to establish an automatic landslide interpretation model. This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG) feature extraction model. In order to test the effectiveness of the method, typical landslide images were selected to construct a landslide sample library, which was subsequently utilized as the foundation for conducting an experimental study. The results show that the accuracy of landslide extraction using this method reaches as high as 89%, indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas, and has high practical value for regional disaster prevention and damage reduction.