Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery
AbstractThis 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. View Full-Text
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Kim, S.-R.; Lee, W.-K.; Kwak, D.-A.; Biging, G.S.; Gong, P.; Lee, J.-H.; Cho, H.-K. Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery. Sensors 2011, 11, 1943-1958.
Kim S-R, Lee W-K, Kwak D-A, Biging GS, Gong P, Lee J-H, Cho H-K. Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery. Sensors. 2011; 11(2):1943-1958.Chicago/Turabian Style
Kim, So-Ra; Lee, Woo-Kyun; Kwak, Doo-Ahn; Biging, Greg S.; Gong, Peng; Lee, Jun-Hak; Cho, Hyun-Kook. 2011. "Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery." Sensors 11, no. 2: 1943-1958.