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Proceedings Paper

Low-dimensional representations of hyperspectral data for use in CRF-based classification
Author(s): Yang Hu; Nathan D. Cahill; Sildomar T. Monteiro; Eli Saber; David W. Messinger
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Paper Abstract

Probabilistic graphical models have strong potential for use in hyperspectral image classification. One important class of probabilisitic graphical models is the Conditional Random Field (CRF), which has distinct advantages over traditional Markov Random Fields (MRF), including: no independence assumption is made over the observation, and local and pairwise potential features can be defined with flexibility. Conventional methods for hyperspectral image classification utilize all spectral bands and assign the corresponding raw intensity values into the feature functions in CRFs. These methods, however, require significant computational efforts and yield an ambiguous summary from the data. To mitigate these problems, we propose a novel processing method for hyperspectral image classification by incorporating a lower dimensional representation into the CRFs. In this paper, we use representations based on three types of graph-based dimensionality reduction algorithms: Laplacian Eigemaps (LE), Spatial-Spectral Schroedinger Eigenmaps (SSSE), and Local Linear Embedding (LLE), and we investigate the impact of choice of representation on the subsequent CRF-based classifications.

Paper Details

Date Published: 15 October 2015
PDF: 8 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430L (15 October 2015); doi: 10.1117/12.2195229
Show Author Affiliations
Yang Hu, Rochester Institute of Technology (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)
Sildomar T. Monteiro, Rochester Institute of Technology (United States)
Eli Saber, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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