Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 25th Anniversary IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium |
| Pages | 172-175 |
| Number of pages | 4 |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, Korea, Republic of Duration: Jul 25 2005 → Jul 29 2005 |
Other
| Other | 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 7/25/05 → 7/29/05 |
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All Science Journal Classification (ASJC) codes
- Geology
- Software
Cite this
Spatio-temporal contextual image classification based on spatial AdaBoost. / Nishii, Ryuei; Eguchi, Shinto.
25th Anniversary IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium. Vol. 1 2005. p. 172-175 1526132.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spatio-temporal contextual image classification based on spatial AdaBoost
AU - Nishii, Ryuei
AU - Eguchi, Shinto
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33745699902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745699902&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2005.1526132
DO - 10.1109/IGARSS.2005.1526132
M3 - Conference contribution
AN - SCOPUS:33745699902
SN - 0780390504
SN - 9780780390508
VL - 1
SP - 172
EP - 175
BT - 25th Anniversary IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium
ER -