A Hyperheuristic Approach for Unsupervised Land-Cover Classification

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@Article{Papa:2016:ieeeAEORS,

• author = "Joao Papa Papa and Luciene Patrici Papa and Danillo Roberto Pereira and Rodrigo Jose Pisani",
• journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
• title = "A Hyperheuristic Approach for Unsupervised Land-Cover Classification",
• year = "2016",
• volume = "9",
• number = "6",
• pages = "2333--2342",
• abstract = "Unsupervised land-use/cover classification is of great interest, since it becomes even more difficult to obtain high-quality labelled data. Still considered one of the most used clustering techniques, the well-known k-means plays an important role in the pattern recognition community. Its simple formulation and good results in a number of applications have fostered the development of new variants and methodologies to address the problem of minimizing the distance from each dataset sample to its nearest centroid (mean). In this paper, we present a genetic programming-based hyperheuristic approach to combine different metaheuristic techniques used to enhance k-means effectiveness. The proposed approach is evaluated in four satellite and one radar image showing promising results, while outperforming each individual metaheuristic technique.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/JSTARS.2016.2557584",
• ISSN = "1939-1404",
• month = jun,
• notes = "Also known as \cite{7471415}",

}

Genetic Programming entries for Joao Paulo Papa Luciene Patrici Papa Danillo Roberto Pereira Rodrigo Jose Pisani

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