Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

 

So-Ra Kim, Woo-Kyun Lee, Doo-Ahn Kwak, Greg S. Biging, Peng Gong,

Jun-Hak Lee and Hyun-Kook Cho 

 

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.

 

Keywords: digital forest cover map; high resolution; satellite image; pixel-based classification; segment-based classification