Forest cover classification by optimal segmentation of high resolution satellite imagery.
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.
Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136-701, Korea. allwhile@korea.ac.kr
So-Ra
SR
Kim
Woo-Kyun
WK
Lee
Doo-Ahn
DA
Kwak
Greg S
GS
Biging
Peng
P
Gong
Jun-Hak
JH
Lee
Hyun-Kook
HK
Cho
D005843:Geography; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D056910:Republic of Korea; D017215:Satellite Communications; D013057:Spectrum Analysis; D014197:Trees / Q000145:classification*