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- W2912290218 abstract "Ongoing earth monitoring and mapping land-use for cultivated area and land-cover supervision is one of the most important environmental and natural resources projects. Since the traditional methods of regional visiting and on-field data taking are time-consuming and costly, it is preferred to use satellite images with high spatial, time and spectural resolutions for dynamical classification and mapping land-uses. In this study, for land-use mapping of Sanjabi plain in Ravansar city of Kermanshah province, 10-meter bands and 20-meter bands decreased to 10-meters of single time Sentinel-2A satellite image were used. The efficiency of supervised classification methods including Maximum Likelihood, Minimum Distance to Mean and Support Vector Machine using overall accuracy and kappa coefficient criteria was assessed. Results of this comparison revealed that Support Vector Machine classifier with overall accuracy of 88.41 percent and 0.8246 kappa coefficient leads to more satisfying results, rather than other algorithms.Keywords: land-use map, satellite image classification, Maximum Likelihood, Minimum Distance, Support Vector Machine, Sentinel-2" @default.
- W2912290218 created "2019-02-21" @default.
- W2912290218 creator A5016038662 @default.
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- W2912290218 date "2018-01-01" @default.
- W2912290218 modified "2023-10-14" @default.
- W2912290218 title "Classification Accuracy Assesment for Land-use Mapping in Ravansar-Sanjabi Plain using Sentinel-2 Satellite Image" @default.
- W2912290218 hasPublicationYear "2018" @default.
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