Supervised image classification based on AdaBoost with contextual weak classifiers

Ryuei Nishii, Shinto Eguchi

研究成果: 著書/レポートタイプへの貢献会議での発言

5 引用 (Scopus)

抜粋

AdaBoost, one of machine learning techniques, 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 posteriors in various neighborhoods are calculated, and the averages are used as contextual classifiers. Weights for the classifiers can be determined by minimizing the empirical risk with multiclass. Finally, a linear combination of classifier is obtained. The proposed method is applied to artificial multispectral images and shows an excellent performance similar to the MRF-based classifier with much less computation time.

元の言語英語
ホスト出版物のタイトル2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004
ページ1467-1470
ページ数4
2
出版物ステータス出版済み - 2004
イベント2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, 米国
継続期間: 9 20 20049 24 2004

その他

その他2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004
米国
Anchorage, AK
期間9/20/049/24/04

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Geology
  • Software

これを引用

Nishii, R., & Eguchi, S. (2004). Supervised image classification based on AdaBoost with contextual weak classifiers. : 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 (巻 2, pp. 1467-1470)