Forest cover classification by optimal segmentation of high resolution satellite imagery.
Sensors (Basel)
; 11(2): 1943-58, 2011.
Article
in En
| MEDLINE
| ID: mdl-22319391
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Trees
/
Image Processing, Computer-Assisted
/
Satellite Communications
Country/Region as subject:
Asia
Language:
En
Journal:
Sensors (Basel)
Year:
2011
Document type:
Article