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A Technique for Simultaneous Visualization and Segmentation of Hyperspectral Data

DSpace at IIT Bombay

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Title A Technique for Simultaneous Visualization and Segmentation of Hyperspectral Data
 
Creator MEKA, A
CHAUDHURI, S
 
Subject IMAGE FUSION
ALGORITHMS
CLASSIFICATION
MINIMIZATION
REMOVAL
Hyperspectral visualization
segmentation
TV-norm
 
Description In this paper, we propose an optimization-based method for simultaneous fusion and unsupervised segmentation of hyperspectral remote sensing images by exploiting redundancy in the data. The hyperspectral data set is visualized as a single image obtained by weighted addition of all spectral points at each pixel location in the data set. The weights are optimized to improve those statistical characteristics of the fused image, which invoke an enhanced response from a human observer. A piecewise-constant smoothness constraint is imposed on the weights instead of the fused image by minimization of its 3-D total-variation norm, thus preventing the fused image from blurring. The optimal recovery of the weight matrix additionally provides useful information in segmenting the hyperspectral data set spatially. We provide ample experimental results to substantiate the usefulness of the proposed method.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2016-01-15T09:13:38Z
2016-01-15T09:13:38Z
2015
 
Type Article
 
Identifier IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 53(4)1707-1717
0196-2892
1558-0644
http://dx.doi.org/10.1109/TGRS.2014.2346653
http://dspace.library.iitb.ac.in/jspui/handle/100/18247
 
Language en