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Proceedings Paper

Integration of heterogeneous data for classification in hyperspectral satellite imagery
Author(s): J. Benedetto; W. Czaja; J. Dobrosotskaya; T. Doster; K. Duke; D. Gillis
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Paper Abstract

As new remote sensing modalities emerge, it becomes increasingly important to nd more suitable algorithms for fusion and integration of dierent data types for the purposes of target/anomaly detection and classication. Typical techniques that deal with this problem are based on performing detection/classication/segmentation separately in chosen modalities, and then integrating the resulting outcomes into a more complete picture. In this paper we provide a broad analysis of a new approach, based on creating fused representations of the multi- modal data, which then can be subjected to analysis by means of the state-of-the-art classiers or detectors. In this scenario we shall consider the hyperspectral imagery combined with spatial information. Our approach involves machine learning techniques based on analysis of joint data-dependent graphs and their associated diusion kernels. Then, the signicant eigenvectors of the derived fused graph Laplace operator form the new representation, which provides integrated features from the heterogeneous input data. We compare these fused approaches with analysis of integrated outputs of spatial and spectral graph methods.

Paper Details

Date Published: 24 May 2012
PDF: 12 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839027 (24 May 2012); doi: 10.1117/12.919236
Show Author Affiliations
J. Benedetto, Univ. of Maryland, College Park (United States)
W. Czaja, Univ. of Maryland, College Park (United States)
J. Dobrosotskaya, Univ. of Maryland, College Park (United States)
T. Doster, Univ. of Maryland, College Park (United States)
K. Duke, Univ. of Maryland, College Park (United States)
D. Gillis, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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