Contextual image classification based on spatial boosting

Ryuei Nishii

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


Spatial AdaBoost proposed by Nishii and Eguchi (TGRS, 2005) is a supervised image classification method. It is a voting machine based on log posterior probabilities at a test pixel and its neighbors. The method can be obtained by less computation effort with respect to a classifier based on Markov random fields, but still shows a similar excellent performance. Further, the method was modified for applying various settings. This paper considers another extension of Spatial Boost. Consider supervised image classification of geospatial data. Suppose that separated training regions with a single land-cover class are given. In this case, the original Spatial Boost does not work because it incorporates spatial information of the training data. The aim of the paper is to make Spatial Boost applicable to the case. We propose a classifier given by a linear combination of log posteriors whose coefficients are determined by spatial information of test data only. By numerical examples, it shows an excellent performance.

Original languageEnglish
Title of host publication2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Number of pages4
Publication statusPublished - 2006
Event2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States
Duration: Jul 31 2006Aug 4 2006

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Other2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Country/TerritoryUnited States
CityDenver, CO

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences


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