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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages527-529
Number of pages3
ISBN (Print)9781424495658, 9781424495665
DOIs
Publication statusPublished - 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
Duration: 2010 Jul 252010 Jul 30

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Country/TerritoryUnited States
CityHonolulu, HI
Period10-07-2510-07-30

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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