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 Iterative self-organizing data
analysis technique (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 allows 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 (unsupervised genetic algorithm (UGA), 71.49%), differential evolution (unsupervised differential evolution (UDE), 77.57%), and particle swarm optimization (unsupervised 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 one 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.
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Document Type: Research Article
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Griffith School of Environment, Griffith University, Gold Coast, Australia
Publication date: December 16, 2016
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