Contextual unmixing of geospatial data based on Markov random fields and conditional random fields

Ryuei Nishii; Tomohiko Ozaki

doi:10.1109/WHISPERS.2009.5289004

Contextual unmixing of geospatial data based on Markov random fields and conditional random fields

Ryuei Nishii, Tomohiko Ozaki

Division of Advanced Mathematics Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRFs) improve the performance of non-contextual classifiers. In this paper, we consider the unsupervised unmixing problem based on MRFs. The exact solutions maximizing local conditional densities are derived, and they show excellent performance for unximing of data sets. Furthermore a new stochastic model based on conditional random fields is proposed for unmixing of hyperspectral data. The approximation formula of its normalizing factor is also derived.

Original language	English
Title of host publication	WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing
DOIs	https://doi.org/10.1109/WHISPERS.2009.5289004
Publication status	Published - Dec 21 2009
Event	WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France Duration: Aug 26 2009 → Aug 28 2009

Publication series

Name	WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Other

Other	WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Country	France
City	Grenoble
Period	8/26/09 → 8/28/09

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/WHISPERS.2009.5289004

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Nishii, R., & Ozaki, T. (2009). Contextual unmixing of geospatial data based on Markov random fields and conditional random fields. In WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing [5289004] (WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing). https://doi.org/10.1109/WHISPERS.2009.5289004

Contextual unmixing of geospatial data based on Markov random fields and conditional random fields. / Nishii, Ryuei; Ozaki, Tomohiko.

WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. 2009. 5289004 (WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nishii, R & Ozaki, T 2009, Contextual unmixing of geospatial data based on Markov random fields and conditional random fields. in WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing., 5289004, WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France, 8/26/09. https://doi.org/10.1109/WHISPERS.2009.5289004

Nishii R, Ozaki T. Contextual unmixing of geospatial data based on Markov random fields and conditional random fields. In WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. 2009. 5289004. (WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing). https://doi.org/10.1109/WHISPERS.2009.5289004

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