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® 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.
| Original language | English |
|---|---|
| Pages (from-to) | 1943-1958 |
| Number of pages | 16 |
| Journal | Sensors |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2011 Feb 1 |
Fingerprint
Keywords
- Digital forest cover map
- High resolution
- Pixel-based classification
- Satellite image
- Segment-based classification
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
- Analytical Chemistry
- Biochemistry
Cite this
Forest cover classification by optimal segmentation of high resolution satellite imagery. / Kim, So Ra; Lee, Woo-Kyun; Kwak, Doo Ahn; Biging, Greg S.; Gong, Peng; Lee, Jun Hak; Cho, Hyun Kook.
In: Sensors, Vol. 11, No. 2, 01.02.2011, p. 1943-1958.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Forest cover classification by optimal segmentation of high resolution satellite imagery
AU - Kim, So Ra
AU - Lee, Woo-Kyun
AU - Kwak, Doo Ahn
AU - Biging, Greg S.
AU - Gong, Peng
AU - Lee, Jun Hak
AU - Cho, Hyun Kook
PY - 2011/2/1
Y1 - 2011/2/1
N2 - 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.
AB - 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.
KW - Digital forest cover map
KW - High resolution
KW - Pixel-based classification
KW - Satellite image
KW - Segment-based classification
UR - http://www.scopus.com/inward/record.url?scp=79952074186&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952074186&partnerID=8YFLogxK
U2 - 10.3390/s110201943
DO - 10.3390/s110201943
M3 - Article
C2 - 22319391
AN - SCOPUS:79952074186
VL - 11
SP - 1943
EP - 1958
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-8220
IS - 2
ER -