LANGUAGE IN INDIA

Strength for Today and Bright Hope for Tomorrow

Volume 15:3 March 2015
ISSN 1930-2940

Managing Editor: M. S. Thirumalai, Ph.D.
Editors: B. Mallikarjun, Ph.D.
         Sam Mohanlal, Ph.D.
         B. A. Sharada, Ph.D.
         A. R. Fatihi, Ph.D.
         Lakhan Gusain, Ph.D.
         Jennifer Marie Bayer, Ph.D.
         S. M. Ravichandran, Ph.D.
         G. Baskaran, Ph.D.
         L. Ramamoorthy, Ph.D.
         C. Subburaman, Ph.D. (Economics)
         N. Nadaraja Pillai, Ph.D.
Assistant Managing Editor: Swarna Thirumalai, M.A.

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Structure Representation for Hyper spectral Images
Using Binary Classification

R. Revathi M.E., Ph.D. Candidate

Abstract

Binary Partition Trees are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the Search space for the object detection task. The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical structure representation for such images using binary partition trees. Based on region merging techniques using statistical measures, this region-based representation reduces the number of elementary primitives and allows a more robust filtering, segmentation, classification or information retrieval. To demonstrate BPT capabilities, we first discuss the construction of BPT in the specific framework of hyperspectral data. We then propose a pruning strategy in order to perform a classification. Labeling each BPT node with SVM classifiers outputs, a pruning decision based on an impurity measure is addressed. Experimental results on two different hyperspectral data sets have demonstrated the good performances of a BPT-based representation.

Keywords: Hyperspectral imaging, Binary classification, segmentation, BPT Pruning, Region model

1. INTRODUCTION

Recent advances in remote sensing and geographic information has led the way for the development of hyperspectral sensors which produce a data cube of hundreds of contiguous waveband images. Therefore, each pixel is represented by a spectrum related to the light absorbing and/or scattering properties of the spatial region that it represents. Given the wide range of real-life applications, great deal of research is invested in the field of hyperspectral image segmentation. The segmentation of these images is a key step in their analysis. Unfortunately, hyperspectral image processing is still a difficult endeavor due to the huge amount of data involved. Consequently, most of the standard segmentation methods fail.

This is only the beginning part of the article. PLEASE CLICK HERE TO READ THE ENTIRE ARTICLE IN PRINTER-FRIENDLY VERSION.

R. Revathi
Associate Professor
Department of Information Technology
Dhanalakshmi Srinivasan Engineering College
Perambalur 621212
Tamilnadu
India
sriramrr2007@gmail.com
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