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7 May 2014 Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms
Tao He, Yu-Jun Sun, Ji-De Xu, Xue-Jun Wang, Chang-Ru Hu
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Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083636 (May 2014). https://doi.org/10.1117/1.JRS.8.083636
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Tao He, Yu-Jun Sun, Ji-De Xu, Xue-Jun Wang, and Chang-Ru Hu "Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms," Journal of Applied Remote Sensing 8(1), 083636 (7 May 2014). https://doi.org/10.1117/1.JRS.8.083636
Published: 7 May 2014
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Cited by 21 scholarly publications and 1 patent.
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KEYWORDS
Image classification
Image fusion
Remote sensing
Fuzzy logic
Image segmentation
Vegetation
Forestry
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