Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling

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 publication2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages3104-3107
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia
Duration: Jul 21 2013Jul 26 2013

Other

Other2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
CountryAustralia
CityMelbourne, VIC
Period7/21/137/26/13

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling'. Together they form a unique fingerprint.

  • Cite this

    Nishii, R., Qin, P., & Uchi, D. (2013). Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings (pp. 3104-3107). [6723483] https://doi.org/10.1109/IGARSS.2013.6723483