A Technique for Simultaneous Visualization and Segmentation of Hyperspectral Data

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...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2015-04, Vol.53 (4), p.1707-1717
Main Authors: Meka, Abhimitra, Chaudhuri, Subhasis
Format: Article
Language:English
Subjects:
Arrays
Data visualization
Datasets
Human
Hyperspectral imaging
Hyperspectral visualization
Image enhancement
Image fusion
Image segmentation
Norms
Optimization
Remote sensing
Satellites
Segmentation
Spectra
Three dimensional
TV-norm
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2014.2346653