TY - GEN
T1 - Cluster-based ensemble classification for hyperspectral remote sensing images
AU - Chi, Mingmin
AU - Qian, Qun
AU - Benediktsson, Jón Atli
PY - 2008
Y1 - 2008
N2 - Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multispectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides, the training samples may not represent the real distribution of the whole space. To attack the quality problems of training samples, we proposed a Cluster-based ENsemble Algorithm (CENA) for the classification of hyperspectral remote sensing images. Data set collected from ROSIS university validates the effectiveness of the proposed approach.
AB - Hyperspectral remote sensing images play a very important role in the discrimination of spectrally similar land-cover classes. In order to obtain a reliable classifier, a larger amount of representative training samples are necessary compared to multispectral remote sensing data. In real applications, it is difficult to obtain a sufficient number of training samples for supervised learning. Besides, the training samples may not represent the real distribution of the whole space. To attack the quality problems of training samples, we proposed a Cluster-based ENsemble Algorithm (CENA) for the classification of hyperspectral remote sensing images. Data set collected from ROSIS university validates the effectiveness of the proposed approach.
KW - Ensemble
KW - Hyperspectral remote sensing images.
KW - Mixture of Gaussian (MoG)
KW - Support Cluster Machine (SCM)
UR - http://www.scopus.com/inward/record.url?scp=67649779171&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2008.4778830
DO - 10.1109/IGARSS.2008.4778830
M3 - Conference contribution
AN - SCOPUS:67649779171
SN - 9781424428083
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - I209-I212
BT - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
T2 - 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings
Y2 - 6 July 2008 through 11 July 2008
ER -