Landslide identification based on FORMOSAT-2 multispectral imagery by wavelet-based texture feature extraction

Li Wei Chang, Pi Fuei Hsieh, Ching Weei Lin

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

5 Citations (Scopus)

Abstract

To identify landslides for disaster monitoring, FORMOSAT-2 imagery has the advantages of low cost and frequent revisit over any other satellite imagery currently available in Taiwan. However, its four spectral bands are not capable enough to distinguish landslides from other ground cover types, for example, thin rivers. In this study, we attempt to overcome the spectral incapability of FORMOSAT-2 imagery from the standpoint of classification. First, we explore more discriminative features, such as texture and topographical features, in order to improve class separability. Texture features are extracted from the FORMOSAT-2 imagery itself using the log-polar wavelet packet transformation whereas a topographical feature slope is derived from an auxiliary Digital Elevation Model (DEM) dataset. Second, a contextual classifier that combines spectral and spatial information is used since this type of classifier is appropriate for our case of homogeneous object identification. Our results have been validated partially by field investigation on several sites. Experiments show that our approach has given a significant improvement over the spectral approach.

Original languageEnglish
Title of host publication2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3300-3303
Number of pages4
ISBN (Print)0780395107, 9780780395107
DOIs
Publication statusPublished - 2006
Event2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States
Duration: 2006 Jul 312006 Aug 4

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

Other2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
CountryUnited States
CityDenver, CO
Period06-07-3106-08-04

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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