期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 7, 期 4, 页码 1060-1069出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2014.2301775
关键词
Bagging-based ensemble extreme learning machines (BagELMs); boostELMs; classification; ensemble extreme learning machines ((ELMs)-L-2); ensemble learning (EL); extreme learning machine (ELM); hyperspectral remote sensing
类别
资金
- Jiangsu Provincial Natural Science Foundation [BK2010182]
- Natural Science Foundation of China [41171323]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
Extreme learning machine (ELM) has attracted attentions in pattern recognition field due to its remarkable advantages such as fast operation, straightforward solution, and strong generalization. However, the performance of ELM for high-dimensional data, such as hyperspectral image, is still an open problem. Therefore, in this paper, we introduce ELM for hyperspectral image classification. Furthermore, in order to overcome the drawbacks of ELM caused by the randomness of input weights and bias, two new algorithms of ensemble extreme learning machines (Bagging-based and AdaBoost-based ELMs) are proposed for the classification task. In order to illustrate the performance of the proposed algorithms, support vector machines (SVMs) are used for evaluation and comparison. Experimental results with real hyperspectral images collected by reflective optics spectrographic image system (ROSIS) and airborne visible/infrared imaging spectrometer (AVIRIS) indicate that the proposed ensemble algorithms produce excellent classification performance in different scenarios with respect to spectral and spectral-spatial feature sets.
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