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BioMed Research International
Volume 2016, Article ID 8797438, 12 pages
http://dx.doi.org/10.1155/2016/8797438
Research Article

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

1Department of CS & IT, The Islamia University of Bahawalpur, Punjab 63100, Pakistan
2Department of Computer Sciences, CIIT Lahore, Punjab 54000, Pakistan
3Key Laboratory of Photo-Electronic Imaging Technology and System, School of Computer Science, Beijing Institute of Technology (BIT), Beijing 100081, China
4Department of Bioinformatics and Computational Biology, Virtual University of Pakistan, Lahore, Punjab 54000, Pakistan
5Department of CS, NFC IET, Multan, Punjab 60000, Pakistan
6Department of CS, Virtual University of Pakistan, Lahore, Punjab 54000, Pakistan
7Faculty of Information Technology, University of Central Punjab (UCP), Lahore 54000, Pakistan

Received 26 November 2015; Accepted 28 April 2016

Academic Editor: John P. Geisler

Copyright © 2016 Salman Qadri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively.