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

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)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.28, no.5, 2010 , pp. 493-504 More about this Journal
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
Classification; Automated training; multi-sensor images; non-accessible area;
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