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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3258

Title: Remotely Sensed Image Classification Technique for Tea Plantation in Sri Lanka
Authors: Mahavidanage, M.D.S.R.A.
Issue Date: 19-Nov-2015
Abstract: In Geographic Information Systems, supervised learning is traditionally used to classify remotely sensed imagery data to develop land-use maps. The classifiers used for this process tend to generate inconsequent classes due to the variety of reasons. To understand the problem, several experiments were done by using a number of commonly used unsupervised and supervised image classifiers namely K-means clustering, ISODATA clustering, Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped and Binary Encoding. These experiments were done using the ERDAS Imagine and ENVI image processing software applications. The sample image chosen is a subset of Quick Bird-2008 Satellite imagery of Ganga Ihala Korale divisionin Kandy district in Sri Lanka covering an extent of 5.73ha. None of the approaches experimented generated the expected land-use maps for the tea plantation. Although Mahalanobis Distance classifier achieves the highest accuracy in confusion matrix, the result is not suitable to use for a land-use map as it generates a number ofinconsistent and compound classes.Thus, this study is aimed at exploring a new approach to identify the tea plantation in Sri Lanka for land-use mapping while reducing the misclassification errors generated by the other approaches. The proposed approach is based on the supervised classification approach which uses the conventional minimum distance decision rule. It incorporates a spatial object based threshold scheme to reduce the complexity of the classification and to reduce the effect of the misclassification problem. The proposed method is tested for upcountry tea plantation of Sri Lanka. Two subsets of Quick Bird-2008 Satellite imagery in Kandy district are used to evaluate the proposed approach. The results show that the pixels are correctly classified to the correct classes in the GIS layer which consists of spatial boundaries of land-use types. The comparison of the results of proposed method with the conventional method reveals higher accuracy in proposed method.
URI: http://hdl.handle.net/123456789/3258
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