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City Leuven / St. Paul / Minneapolis / New York / / Company ERDAS Inc. / Cox / Khoral Research Inc. / Elsevier Science Inc. / / Continent Americas / / Country Belgium / United States / / / Facility College of Natural Resources / / IndustryTerm medium resolution products / digital satellite imagery / linkage region growing algorithms / segmentation algorithms / classification product / region growing algorithms / satellite imagery / software environment / neural networks / segmentation algorithm / image processing environment / earth observation applications / linear operator / search window / operational optical satellite sensors / detection algorithm / detection algorithms / image processing system / digital processing / iterative region growing algorithm / large-area applications / ready-to-use algorithms / hybrid segmentation algorithms / / NaturalFeature Minnesota river / Minnesota River Valley / Mississippi river / St. Croix river / / Organization Department of Land Management / U.S. Department of Agriculture / Federal Communications Commission / Metropolitan Council / Faculty of Agricultural and Applied Biological Sciences / Agricultural Stabilization and Conservation Service / College of Natural Resources / Katholieke Universiteit Leuven / University of Minnesota Address / / Person Ventura / Vital Decosterstraat / Harris / / Position producer / / ProvinceOrState Minnesota / Mississippi / New York / / PublishedMedium the ECHO / / Technology merging algorithm / MLH algorithm / Segmentation algorithms / image processing system / segmentation algorithm / ECHO algorithm / detection algorithms / ready-to-use algorithms / Woodcock-Haward algorithm / image processing / detection algorithm / hybrid segmentation algorithms / / SocialTag 
Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification J. Stuckens,* P. R. Coppin* and M. E. Bauer†A