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

Ryuei Nishii, T. Ozaki, Y. Sawamura

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
Volume2
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: Jul 12 2009Jul 17 2009

Other

Other2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
CountrySouth Africa
CityCape Town
Period7/12/097/17/09

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)
  • Computer Science Applications

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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] https://doi.org/10.1109/IGARSS.2009.5418071