%0 Journal Article
%T Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery
%A So-Ra Kim
%A Woo-Kyun Lee
%A Doo-Ahn Kwak
%A Greg S. Biging
%A Peng Gong
%A Jun-Hak Lee
%A Hyun-Kook Cho
%J Sensors
%D 2011
%I MDPI AG
%R 10.3390/s110201943
%X This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens£¿ Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the ¡°salt-and-pepper effect¡± and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.
%K digital forest cover map
%K high resolution
%K satellite image
%K pixel-based classification
%K segment-based classification
%U http://www.mdpi.com/1424-8220/11/2/1943