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http://dx.doi.org/10.11108/kagis.2016.19.2.098

Improving Accuracy of Land Cover Classification in River Basins using Landsat-8 OLI Image, Vegetation Index, and Water Index  

PARK, Ju-Sung (School of Convergence & Fusion System Engineering, Kyungpook National University)
LEE, Won-Hee (School of Convergence & Fusion System Engineering, Kyungpook National University)
JO, Myung-Hee (School of Convergence & Fusion System Engineering, Kyungpook National University)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.19, no.2, 2016 , pp. 98-106 More about this Journal
Abstract
Remote sensing is an efficient technology for observing and monitoring the land surfaces inaccessible to humans. This research proposes a methodology for improving the accuracy of the land cover classification using the Landsat-8 operational land imager(OLI) image. The proposed methodology consists of the following steps. First, the normalized difference vegetation index(NDVI) and normalized difference water index(NDWI) images are generated from the given Landsat-8 OLI image. Then, a new image is generated by adding both NDVI and NDWI images to the original Landsat-8 OLI image using the layer-stacking method. Finally, the maximum likelihood classification(MLC), and support vector machine(SVM) methods are separately applied to the original Landsat-8 OLI image and new image to identify the five classes namely water, forest, cropland, bare soil, and artificial structure. The comparison of the results shows that the utilization of the layer-stacking method improves the accuracy of the land cover classification by 8% for the MLC method and by 1.6% for the SVM method. This research proposes a methodology for improving the accuracy of the land cover classification by using the layer-stacking method.
Keywords
Maximum Likelihood Classification(MLC); Support Vector Machine(SVM); Normalized Difference Vegetation Index; Normalized Difference Water Index; Landsat Images;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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