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Proceedings Paper

Multi-resolution segmentation for improved hyperspectral mapping
Author(s): F. A. Kruse
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Paper Abstract

Many hyperspectral imagery (HSI) mapping methods currently attempt to determine the dimensionality of a dataset and extract discreet endmembers based on linear spectral mixing theory. The problem with this approach is that these datasets are often of such high dimensionality that it is difficult to extract the level of detail inherent in the data. Most such analysis approaches are simply overwhelmed by the complexity of HSI data. This research describes an approach that uses segmentation and iterative analysis of HSI data to reduce the dimensionality to a manageable level. The methodology involves spectral/spatial segmentation to determine initial groups of materials. The segmentation can be done using a variety of methods, including classical supervised or unsupervised classification methods, the Spectral Angle Mapper (SAM), spectral feature-based methods, or standard endmember determination and mapping approaches. The result of the segmentation is a broadly classified image. There may be significant variation within each class. These segments are then used as the starting point for additional n-Dimensional analysis. The HSI data are analyzed for each of the classes or segments using a linear mixing approach, endmembers are determined, and distributions and abundances are mapped. The segmentation reduces the original, complex dataset to a series of less complex problems. Combination of the segment results to a composite analysis result produces a materials map that includes additional detail beyond that achieved using the whole-image approaches. A case history utilizing AVIRIS data is presented.

Paper Details

Date Published: 1 June 2005
PDF: 11 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.604748
Show Author Affiliations
F. A. Kruse, Horizon GeoImaging, LLC (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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