Omkar, SN and Kumar, Manoj and Muley, Dipti (2007) Urban satellite image classification using biologically inspired techniques. In: IEEE International Symposium on Industrial Electronics, JUN 04-07, 2007, Vigo.
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This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)|
|Date Deposited:||10 Jun 2010 09:42|
|Last Modified:||21 Oct 2011 08:23|
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