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Remote Sensing Image Classification Using kNN Algorithm

Authors(5):

Kshitij Gadwe, Nikhil Waghchoure, Shridhar Gokule, Hemant Reddypatil, Prof. Minakshi Vharkate
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K-nearest neighbor (KNN) is a common classification method for data mining techniques. It has been widely used in many fields because of the implementation simplicity, the clarity of theory and the excellent classification performance. But KNN will increase classification error rate when training samples distribute unevenly or sample number of each class is very different. So, learning from the idea of clipping-KNN, this paper adopts an improved KNN classification algorithm and applies it to object-oriented classification of high resolution remote sensing image. Firstly, as sample points, image objects are obtained through image segmentation. Secondly, original KNN, clipping-KNN and the improved KNN are introduced and used to classify those sample points respectively. Finally, classification results are compared. Experiment shows that in the same training set and testing set, the improved KNN algorithm can achieve higher accuracy in the classification of high resolution remote sensing image.

Kshitij Gadwe, Nikhil Waghchoure, Shridhar Gokule, Hemant Reddypatil, Prof. Minakshi Vharkate

KNN classification; high resolution remote sensing image; object-oriented; segmentation.

Publication Details

Published in : Volume 2 | Issue 2 | March-April - 2016
Date of Publication Print ISSN Online ISSN
2016-04-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
194-195 IJSRSET1621240   Technoscience Academy

Cite This Article

Kshitij Gadwe, Nikhil Waghchoure, Shridhar Gokule, Hemant Reddypatil, Prof. Minakshi Vharkate, "Remote Sensing Image Classification Using kNN Algorithm", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.194-195, March-April-2016.
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