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Land-use Classification Method Based on Landsat8 OLI Images

  

  1. College of Information Engineering, Xizang Minzu University, Xianyang 712082, China
  • Received:2015-07-29 Online:2015-09-21 Published:2015-09-24

Abstract:

This research aims to seek out the most suitable land use classification method for Landsat8 OLI images, by comparing 〖JP2〗supervised classification based on
maximum likelihood and the method proposed in this paper. There are 11 kinds of land use type in OLI images of Dingbian county. Firstly, OLI images were fused with panchromatic
image and then processed by 3 levels wavelet filtering after routine image preprocessing. Secondly, LBV transform were applied to OLI images. Thirdly, training samples set for
each kind land use type were collected, and then supervised classification based on SVM, opening-closing operation in mathematical 〖JP2〗morphology were carried out to get
precise information of each kind land use type. Fourthly, assessing the classification results of method proposed in this paper and maximum likelihood, by overall classification
accuracy and Kappa coefficient as evaluation indexes. Results show that: the overall accuracy and Kappa coefficient of classification image using the method proposed in this
paper were 83.62% and 0.785, with growth of 12.82% and 14.26% compared with classification image using maximum likelihood. Meanwhile, removal of salt and pepper noise in
classification image was more effective using method proposed in this paper.

Key words: image classification, Dingbian county, support vector machine, wavelet filtering