Contextual unmixing of geospatial data based on gaussian mixture models and markov random fields

R. Nishii, Y. Sawamura, A. Nakamoto, S. Kawaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In supervised and unsupervised image classification, it is known that contextual classificationmethods based onMarkov random fields (MRF) improve non-contextual classifiers successfully. In this paper, we consider unsupervised unmixing problem by introduction of a new MRF. First, spectral vectors observed at mixels are assumed to follow Gaussian mixtures. Second, vectors representing fractions of categories are supposed to follow MRF over the observed area. Then, we derive an unsupervised unmixing method, which is also useful for unsupervised classification. The proposed method was evaluated through a synthetic data set and a benchmark data set for classification, and it showed an excellent performance.

Original languageEnglish
Title of host publication2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
PagesIV77-IV80
Edition1
DOIs
Publication statusPublished - Dec 1 2008
Event2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States
Duration: Jul 6 2008Jul 11 2008

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Number1
Volume4

Other

Other2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
CountryUnited States
CityBoston, MA
Period7/6/087/11/08

    Fingerprint

All Science Journal Classification (ASJC) codes

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

Cite this

Nishii, R., Sawamura, Y., Nakamoto, A., & Kawaguchi, S. (2008). Contextual unmixing of geospatial data based on gaussian mixture models and markov random fields. In 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings (1 ed., pp. IV77-IV80). [4779660] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 4, No. 1). https://doi.org/10.1109/IGARSS.2008.4779660