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Proceedings Paper

Nonparametric image segmentation applied to multispectral thermal imager data
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Paper Abstract

The Multispectral Thermal Imager (MTI) provides a highly informative source of remote sensing data. However, the analysis and exploitation can be very challenging. Effective utilization of this imagery by an image analyst typically requires a consistent and timely means of locating regions of interest. Many available image analysis/segmentation techniques are often slow, not robust to spectral variabilities from view to view or within a spectrally similar region, and/or require a significant amount of user intervention to achieve a segmentation corresponding to self-similar regions within the data. This paper discusses a segmentation approach that exploits the gross spectral shape of MTI data. In particular, we propose a nonparametric approach to perform coarse level segmentation that can stand alone or as a potential precursor to other image analysis tools. In comparison to previous techniques, the key characteristics of this approach are in its simplicity, speed, and consistency. Most importantly it requires relatively few user inputs and determines the number of clusters, their extent, and, data assignment directly from the data.

Paper Details

Date Published: 23 September 2003
PDF: 14 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.488532
Show Author Affiliations
Jose S. Salazar, Sandia National Labs. (United States)
Jody L. Smith, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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