Automatic image classification of landslides improved with terrain roughness indices in various kernel sizes

Mon Shieh Yang, Ming Chang Lin, Jin King Liu, Ming Chee Wu

研究成果: Conference contribution

摘要

Using spectral-only information for landslides classification is usually confusing with houses, roads, and other bare lands because these ground features have similar spectral patterns on images. The terrain roughness can be measured by significant wavelengths; some studies have linked the relationships between terrain roughness and the landslide by using numerical analyses of topography data. In this study, airborne LiDAR data of 1m grid are used to explore the possibility of improvement of landslide classification, the LiDAR-derived data include DEM slope and terrain roughness indices including diversity, dominance and relative richness with different grid size data are used to improvement classification accuracy. The improvement of accuracy when including DEM slope is 22% in producer's accuracy and 27% in user's accuracy. The accuracy of diversity, dominance and relative richness indices all are improved when kernel sizes enlarge in Maximum Likelihood and Mahalanobis Distance algorithms.

原文English
主出版物標題2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
發行者Institute of Electrical and Electronics Engineers Inc.
頁面527-529
頁數3
ISBN(列印)9781424495658, 9781424495665
DOIs
出版狀態Published - 2010
事件2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
持續時間: 2010 七月 252010 七月 30

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
國家/地區United States
城市Honolulu, HI
期間10-07-2510-07-30

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 地球與行星科學(全部)

指紋

深入研究「Automatic image classification of landslides improved with terrain roughness indices in various kernel sizes」主題。共同形成了獨特的指紋。

引用此