Paper Info
Title
Forest cover classification by optimal segmentation of high resolution satellite imagery.
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.
Year
DOI
Venue
2011
10.3390/s110201943
SENSORS
Keywords
Field
DocType
digital forest cover map,high resolution,satellite image,pixel-based classification,segment-based classification
Computer vision,Satellite,Satellite imagery,Forest cover,Segmentation,Artificial intelligence,Pixel,Communications satellite,Majority rule,Geography,Scale parameter
Journal
Volume
Issue
ISSN
11
2
1424-8220
Citations 
PageRank 
References 
10
0.90
3
Authors
7
Name
Order
Citations
PageRank
So-Ra Kim1100.90
Woo-Kyun Lee2234.08
Doo-Ahn Kwak3100.90
Greg S. Biging46913.80
Peng Gong544274.30
Jun-Hak Lee6111.27
Hyun-Kook Cho7101.24