Land cover classification of a non-accessible area using multi-sensor images and GIS data

다중센서와 GIS 자료를 이용한 접근불능지역의 토지피복 분류

  • Kim, Yong-Min (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Park, Wan-Yong (Agency for Defense Development) ;
  • Eo, Yang-Dam (Department of Advanced Technology Fusion, Konkuk University) ;
  • Kim, Yong-Il (Department of Civil and Environmental Engineering, Seoul National University)
  • Received : 2010.07.29
  • Accepted : 2010.09.14
  • Published : 2010.10.31

Abstract

This study proposes a classification method based on an automated training extraction procedure that may be used with very high resolution (VHR) images of non-accessible areas. The proposed method overcomes the problem of scale difference between VHR images and geographic information system (GIS) data through filtering and use of a Landsat image. In order to automate maximum likelihood classification (MLC), GIS data were used as an input to the MLC of a Landsat image, and a binary edge and a normalized difference vegetation index (NDVI) were used to increase the purity of the training samples. We identified the thresholds of an NDVI and binary edge appropriate to obtain pure samples of each class. The proposed method was then applied to QuickBird and SPOT-5 images. In order to validate the method, visual interpretation and quantitative assessment of the results were compared with products of a manual method. The results showed that the proposed method could classify VHR images and efficiently update GIS data.

Keywords

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