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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 26/10/2016, available online: http://www.tandfonline.com/10.1080/01431161.2016.1246771

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A novel unsupervised bee colony optimization (UBCO) method for remote sensing image classification: a case study in a heterogeneous marsh area

Research output: Contribution to journalJournal article

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  • Huapeng Li
  • Shuqing Zhang
  • Xiaohui Ding
  • Ce Zhang
  • Roger Cropp
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<mark>Journal publication date</mark>27/10/2016
<mark>Journal</mark>International Journal of Remote Sensing
Issue number24
Volume37
Number of pages23
Pages (from-to)5627-5748
Publication statusPublished
Early online date26/10/16
Original languageEnglish

Abstract

Unsupervised image classification is an important means to obtain land use/cover information
in the field of remote sensing, since it does not require initial knowledge (training samples)
for classification. Traditional methods such as k-means and ISODATA have limitations in
solving this NP-hard unsupervised classification problem, mainly due to their strict
assumptions about the data distribution. The bee colony optimization (BCO) is a new type of
swarm intelligence, based upon which a simple and novel unsupervised bee colony
optimization (UBCO) method is proposed for remote sensing image classification. UBCO
possesses powerful exploitation and exploration capacities that are carried out by employed
bees, onlookers and scouts. This enables the promising regions to be globally searched
quickly and thoroughly, without becoming trapped on local optima. In addition, it has no
restrictions on data distribution, and thus is especially suitable for handling complex remote
sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR)—a
typical inland wetland ecosystem in China, whose landscape is heterogeneous. The
preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically
significant better classification result (McNemar test) in comparison with traditional k-means
(63.11%) and other intelligent clustering methods built on genetic algorithm (UGA, 71.49%),
differential evolution (UDE, 77.57%) and particle swarm optimization (UPSO, 69.86%). The
robustness and superiority of UBCO were also demonstrated from the two other study sites
next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling to
consistently find the optimal or nearly optimal global solution in image clustering, the UBCO
is thus suggested as a robust method for unsupervised remote sensing image classification,
especially in the case of heterogeneous areas.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 26/10/2016, available online: http://www.tandfonline.com/10.1080/01431161.2016.1246771