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Journal of Applied Remote Sensing

Spatial segmentation of multi/hyperspectral imagery by fusion of spectral-gradient-textural attributes
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Paper Abstract

We propose an unsupervised algorithm that utilizes information derived from spectral, gradient, and textural attributes for spatially segmenting multi/hyperspectral remotely sensed imagery. Our methodology commences by determining the magnitude of spectral intensity variations across the input scene, using a multiband gradient detection scheme optimized for handling remotely sensed image data. The resultant gradient map is employed in a dynamic region growth process that is initiated in pixel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprised of an initial set of regions. This region map is combined with several co-occurrence matrix-derived textural descriptors along with intensity and gradient features in a multivariate analysis-based region merging procedure that fuses the regions with similar characteristics to yield the final segmentation output. Our approach was tested on several multi/hyperspectral datasets, and the results show a favorable performance in comparison with state-of-the-art techniques.

Paper Details

Date Published: 31 March 2015
PDF: 37 pages
J. Appl. Remote Sens. 9(1) 095086 doi: 10.1117/1.JRS.9.095086
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Sreenath Rao Vantaram, Rochester Institute of Technology (United States)
Sankaranarayanan Piramanayagam, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)


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