Contextual unmixing of geospatial data based on Bayesian modeling

Ryuei Nishii, Pan Qin, Daisuke Uchi

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

Abstract

Image classification has a long history for estimating landcover categories by feature vectors, and various methods have been proposed from many viewpoints; statistics, machine learning and others. Multivariate normal distributions are frequently used to model feature distributions. Also, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1631-1634
Number of pages4
ISBN (Electronic)9781479957750
DOIs
Publication statusPublished - Nov 4 2014
EventJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada
Duration: Jul 13 2014Jul 18 2014

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Other

OtherJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
CountryCanada
CityQuebec City
Period7/13/147/18/14

All Science Journal Classification (ASJC) codes

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

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    Nishii, R., Qin, P., & Uchi, D. (2014). Contextual unmixing of geospatial data based on Bayesian modeling. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1631-1634). [6946760] (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2014.6946760