Supervised image classification by contextual AdaBoost based on posteriors in neighborhoods

Ryuei Nishii, Shinto Eguchi

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


AdaBoost, a machine learning technique, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels. Then, averages of the log posteriors are calculated in different neighborhoods and are then used as contextual classification functions. Weights for the classification functions can be determined by minimizing the empirical risk with multiclass. Finally, a convex combination of classification functions is obtained. The classification is performed by a noniterative maximization procedure. The proposed method is applied to artificial multispectral images and benchmark datasets. The performance of the proposed method is excellent and is similar to the Markov-random-field-based classifier, which requires an iterative maximization procedure.

Original languageEnglish
Pages (from-to)2547-2554
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11
Publication statusPublished - Nov 2005

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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