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Multiple classifier system for remote sensing image classification: a review

Sensors, 2012-04, Vol.12 (4), p.4764-4792 [Peer Reviewed Journal]

Copyright MDPI AG 2012 ;2012 by the authors; licensee MDPI, Basel, Switzerland. 2012 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s120404764 ;PMID: 22666057

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  • Title:
    Multiple classifier system for remote sensing image classification: a review
  • Author: Du, Peijun ; Xia, Junshi ; Zhang, Wei ; Tan, Kun ; Liu, Yi ; Liu, Sicong
  • Subjects: Algorithms ; classification ; classifier ensemble ; Classifiers ; Communities ; Design engineering ; Image classification ; Literature reviews ; multiple classifier system ; Remote sensing ; Review
  • Is Part Of: Sensors, 2012-04, Vol.12 (4), p.4764-4792
  • Description: 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.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s120404764
    PMID: 22666057
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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