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Journal of Applied Remote Sensing • Open Access

Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms
Author(s): Tao He; Yu-Jun Sun; Ji-De Xu; Xue-Jun Wang; Chang-Ru Hu

Paper Abstract

Land use/cover (LUC) classification plays an important role in remote sensing and land change science. Because of the complexity of ground covers, LUC classification is still regarded as a difficult task. This study proposed a fusion algorithm, which uses support vector machines (SVM) and fuzzy k-means (FKM) clustering algorithms. The main scheme was divided into two steps. First, a clustering map was obtained from the original remote sensing image using FKM; simultaneously, a normalized difference vegetation index layer was extracted from the original image. Then, the classification map was generated by using an SVM classifier. Three different classification algorithms were compared, tested, and verified—parametric (maximum likelihood), nonparametric (SVM), and hybrid (unsupervised-supervised, fusion of SVM and FKM) classifiers, respectively. The proposed algorithm obtained the highest overall accuracy in our experiments.

Paper Details

Date Published: 7 May 2014
PDF: 14 pages
J. Appl. Remote Sens. 8(1) 083636 doi: 10.1117/1.JRS.8.083636
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Tao He, Beijing Forestry Univ. (China)
Zhejiang A&F Univ. (China)
Yu-Jun Sun, Beijing Forestry Univ. (China)
Ji-De Xu, State Forestry Administration (China)
Xue-Jun Wang, State Forestry Inventory and Planning (China)
Chang-Ru Hu, State Forestry Administration (China)

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