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Proceedings Paper

Unmixing and anomaly detection in hyperspectral data due to cluster variation and local information
Author(s): Jochen M. Maerker; Johannes Huber; Wolfgang Middelmann
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Paper Abstract

This paper presents a novel method for anomaly detection based on a cluster unmixing approach. Several algorithms for endmember extraction and unmixing have been reported in literature. Endmember extraction algorithms search for pure materials which constitute the significant structure of the environment. For abundance estimation in hyperspectral imagery, various physically motivated least squares methods are considered. In real hyperspectral data, signatures of each pure material vary with physical texture and perspective. In this work, clustering of data is performed and normal distributions - instead of constant signatures - are used to represent the endmembers. This representation allows determination of class membership by means of unmixing. Furthermore, a parameter optimization is performed. Using only endmembers in a focal window around each pixel better fits the physical model. As result of this local approach, the residual of the reconstruction indicates the magnitude of anomalies. The results obtained with the new approach is called 'Cluster Mixing' (CM). The performance of Cluster Mixing is illustrated by a comparison with other anomaly detection algorithms.

Paper Details

Date Published: 13 May 2010
PDF: 9 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76952G (13 May 2010); doi: 10.1117/12.850293
Show Author Affiliations
Jochen M. Maerker, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Germany)
Johannes Huber, Univ. of Basel (Switzerland)
Wolfgang Middelmann, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Germany)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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