International Journal of Earth Sciences and Engineering

Yuefeng Lu ¹, Shuo Liu ¹, Chen Feng ¹, Jiaxin Zhang ¹

Affiliations
1 Shandong University of Technology, Zibo, Shandong, CN

Abstract

Data are increasingly available from new multi-spectral and high-resolution remote sensors for extracting agricultural information. We conducted a study of extracting apple orchard information from the GF-1 images. Using Yiyuan County in Shandong Province, China as the study area, we compared several different classification methods, including an integrated unsupervised-supervised classification method (improved maximum likelihood), the support vector machine (SVM) method, the nearest-neighbor method, and the decision-tree method. Particularly, we applied the first two methods to the pixel-based classification, and applied the last three methods to object-oriented classification. The comparison finds that the last three methods have higher efficiencies, whereas the first two methods have higher classification accuracy (according to the confusion matrix).

Keywords

Extraction Information, Supervised Classification, Decision-Tree Classification, SVM, Object-Oriented Classification.

Full Text