Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery
- Authors
- Kim, So-Ra; Lee, Woo-Kyun; Kwak, Doo-Ahn; Biging, Greg S.; Gong, Peng; Lee, Jun-Hak; Cho, Hyun-Kook
- Issue Date
- 2월-2011
- Publisher
- MDPI
- Keywords
- digital forest cover map; high resolution; satellite image; pixel-based classification; segment-based classification
- Citation
- SENSORS, v.11, no.2, pp.1943 - 1958
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 11
- Number
- 2
- Start Page
- 1943
- End Page
- 1958
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/113179
- DOI
- 10.3390/s110201943
- ISSN
- 1424-8220
- Abstract
- 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 (R) 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.
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Collections - College of Life Sciences and Biotechnology > Division of Environmental Science and Ecological Engineering > 1. Journal Articles
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