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Sensors 2012, 12(4), 4764-4792;

Multiple Classifier System for Remote Sensing Image Classification: A Review

Department of Geographical Information Science, Nanjing University, Nanjing 210093, China
Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping of China, China University of Mining and Technology, Xuzhou 221116, China
Hebei Bureau of Surveying and Mapping, Shijiazhuang 050031, China
Author to whom correspondence should be addressed.
Received: 22 February 2012 / Revised: 22 March 2012 / Accepted: 6 April 2012 / Published: 12 April 2012
(This article belongs to the Section Remote Sensors)
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Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+).Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community. View Full-Text
Keywords: multiple classifier system; classifier ensemble; remote sensing; classification multiple classifier system; classifier ensemble; remote sensing; classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Du, P.; Xia, J.; Zhang, W.; Tan, K.; Liu, Y.; Liu, S. Multiple Classifier System for Remote Sensing Image Classification: A Review. Sensors 2012, 12, 4764-4792.

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