Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

Jenicka S. (Sethu Institute of Technology, India)
Copyright: © 2018 |Pages: 36
DOI: 10.4018/978-1-5225-5091-4.ch009

Abstract

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.
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Background

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of the current pixel but from the gray level intensity variations of current pixel with its neighbors. The proposed model uses five discrete levels such as -9,-1, 0, 1 and 9 for characterizing the gray level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. For land cover classification, the proposed model MBTP has been used along with fuzzy k-Nearest Neighbor (Fuzzy k-NN) classifier.

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