Contextual image classification based on spatial boosting

Ryuei Nishii

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

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.

本文言語英語
ホスト出版物のタイトル2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
ページ2137-2140
ページ数4
DOI
出版ステータス出版済み - 2006
イベント2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, 米国
継続期間: 7月 31 20068月 4 2006

出版物シリーズ

名前International Geoscience and Remote Sensing Symposium (IGARSS)

その他

その他2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
国/地域米国
CityDenver, CO
Period7/31/068/4/06

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • 地球惑星科学一般

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