Paper Info
Title
A Technique for Simultaneous Visualization and Segmentation of Hyperspectral Data
Abstract
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.
Year
DOI
Venue
2015
10.1109/TGRS.2014.2346653
Geoscience and Remote Sensing, IEEE Transactions  
Keywords
Field
DocType
geophysical image processing,hyperspectral imaging,image fusion,image segmentation,redundancy,remote sensing,3d total variation norm minimization,blurring,data redundancy,hyperspectral data segmentation,hyperspectral data visualization,hyperspectral remote sensing images,optimization based method,piecewise constant smoothness constraint,unsupervised segmentation,hyperspectral visualization,tv-norm,segmentation,optimization,data visualization
Scale-space segmentation,Remote sensing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Computer vision,Full spectral imaging,Pattern recognition,Segmentation,Visualization,Hyperspectral imaging,Pixel,Mathematics
Journal
Volume
Issue
ISSN
53
4
0196-2892
Citations 
PageRank 
References 
5
0.49
22
Authors
2
Name
Order
Citations
PageRank
Abhimitra Meka150.49
Subhasis Chaudhuri21384133.18