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ISPRS Int. J. Geo-Inf. 2013, 2(2), 531-552; doi:10.3390/ijgi2020531

Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover

*  and
Great Valley School of Professional Studies, Penn State University, Malvern, PA 19355, USA
* Author to whom correspondence should be addressed.
Received: 3 April 2013 / Revised: 29 May 2013 / Accepted: 29 May 2013 / Published: 7 June 2013
(This article belongs to the Special Issue Spatial Analysis and Data Mining)
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Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model.
Keywords: content-based image ranking; data mining; ranking; genetic; satellite images; associative content-based image ranking; data mining; ranking; genetic; satellite images;  associative
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Barb, A.; Kilicay-Ergin, N. Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover. ISPRS Int. J. Geo-Inf. 2013, 2, 531-552.

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ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert