@inproceedings{1fd0c825c14c4afd967e32e8be620526,
title = "Joint Adaboost and multifeature based ensemble for hyperspectral image classification",
abstract = "The paper presents a novel ensemble system which unites Adaboost with multifeature to increase diversity among individual classifiers. Adaboost gives rise to convenience for hyperspectral data classification. To improve the method further, we propose joint Adaboost and multifeature based ensemble (JAME), which assigns different multifeature sets to individual classifiers in Adaboost. Diverse spectral and spatial feature sets are integrated to form multifeature sets. As a result, compared with Adaboost the method has increased the diversity of ensemble system, and better overall accuracies are present. Experiments on hyperspectral data sets reveal that the proposed JAME obtains sound performances comparing with original Adaboost and single classifier.",
keywords = "Adaboost, Ensemble, diversity, hyperspectral image classification, multifeature",
author = "Yushi Chen and Xing Zhao and Zhouhan Lin",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",
year = "2014",
month = nov,
day = "4",
doi = "10.1109/IGARSS.2014.6947076",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2874--2877",
booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",
address = "美国",
}