Application of Aerial Ortho-Imagery with Object-Based Image Analysis to Classify Landslide and Neighbor Surface Features

• Main Authors: Fu-Chian Jeng, 鄭傅謙
• Other Authors: Hong-Yuan Lee
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/04405183775294542096

碩士 === 國立臺灣大學 === 土木工程學研究所 === 104 === Recently affected by global warming, it leads to typhoons, floods and other extreme weather occurring more frequency and seriously. During the typhoon and rainy season, it occurs the landslides, debris flow and other disasters more frequently, which making people lose of life and property. Therefore, how to establish the sound and controllable specification as a basis to analyze environmental change and disaster prevention is the problem required to face after getting a large number of high resolution remote sensing data. In this study, based on present knowledge, it builds a simple application of methods and specification. We expect to enhance the application to disaster prevention policies performance. In the past, analysis satellite images by using remote sensing technique has been a major method for detecting landslide among the field of remote sensing. Due to the insufficient degree of spatial resolution, the accuracy of landslides detection is restrained. This study will mainly use the common orthorectified aerial photographs, and digital terrain model (DTM) to automatically classify the images. These provide not only the information of the spectrum, but also combine spatial information to analyze and assist to spectrum which not easy to distinguish objects. In the image classification method, applying Object-based Image Analysis (OBIA) is to process aerial photographs. Besides reducing classify high-resolution images arising the problem of salt and pepper effect, the object are closer to human visual perception. It can be texture, shape, relative position in space and other features to classify images. The results showed that the overall accuracy of the image interpretation can be up to over 87%, meanwhile it is stable, generic, fast. The semi-automated image analysis processing can greatly reduce the time of artificial processing and increase the capacity of handling the large number of images.