Spatial AdaBoost proposed by Nishii and Eguchi (TGRS 2005) is a contextual supervised classifier of land-cover categories of geostatistical data. It shows an excellent performance similar to that of the MRF-based classifier with much less computational cost. In this paper, we extend the method to the setup with multi spatio-temporal images. We take classification functions by the averages of log posterior probabilities derived by respective training data sets. The functions are sequentially combined by minimizing the empirical exponential risk calculated over samples in all the training data sets. Thus, we obtain a classifier based on a convex combination of the functions. The proposed method is applied to artificial data, and it shows performance similar to that of Spatial AdaBoost based on much larger training data.
|ホスト出版物のタイトル||25th Anniversary IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium|
|出版ステータス||出版済み - 2005|
|イベント||2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, 韓国|
継続期間: 7月 25 2005 → 7月 29 2005
|その他||2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005|
|Period||7/25/05 → 7/29/05|
!!!All Science Journal Classification (ASJC) codes