Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions

Ryuei Nishii; T. Ozaki; Y. Sawamura

doi:10.1109/IGARSS.2009.5418071

Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions

Ryuei Nishii, T. Ozaki, Y. Sawamura

Division of Advanced Mathematics Technology

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

1 Citation (Scopus)

Abstract

This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised unmixing method through EM algorithm and ICM method. The proposed method is examined through artificial and real data sets, and shows a excellent performance.

Original language	English
Title of host publication	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/IGARSS.2009.5418071
Publication status	Published - 2009
Event	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa Duration: Jul 12 2009 → Jul 17 2009

Other

Other	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/Territory	South Africa
City	Cape Town
Period	7/12/09 → 7/17/09

All Science Journal Classification (ASJC) codes

Earth and Planetary Sciences(all)
Computer Science Applications

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10.1109/IGARSS.2009.5418071

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Nishii, R, Ozaki, T & Sawamura, Y 2009, Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions. in 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings. vol. 2, 5418071, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Cape Town, South Africa, 7/12/09. https://doi.org/10.1109/IGARSS.2009.5418071

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title = "Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions",

abstract = "This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised unmixing method through EM algorithm and ICM method. The proposed method is examined through artificial and real data sets, and shows a excellent performance.",

author = "Ryuei Nishii and T. Ozaki and Y. Sawamura",

year = "2009",

doi = "10.1109/IGARSS.2009.5418071",

language = "English",

isbn = "9781424433957",

volume = "2",

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note = "2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 ; Conference date: 12-07-2009 Through 17-07-2009",

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AU - Ozaki, T.

AU - Sawamura, Y.

PY - 2009

Y1 - 2009

N2 - This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised unmixing method through EM algorithm and ICM method. The proposed method is examined through artificial and real data sets, and shows a excellent performance.

AB - This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised unmixing method through EM algorithm and ICM method. The proposed method is examined through artificial and real data sets, and shows a excellent performance.

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U2 - 10.1109/IGARSS.2009.5418071

DO - 10.1109/IGARSS.2009.5418071

M3 - Conference contribution

AN - SCOPUS:77951113379

SN - 9781424433957

VL - 2

BT - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings

T2 - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

Y2 - 12 July 2009 through 17 July 2009

ER -

Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions

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