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@InProceedings{HuberDutr:2000:ClCoFe,
               author = "Huber, R. and Dutra, Luciano Vieira",
                title = "Classifier combination and feature selection for land-cover 
                         mapping from high-resolution airborne dual-band SAR data",
            booktitle = "Proceedings...",
                 year = "2000",
                pages = "370--375",
         organization = "World Multiconference on systemics Cybernetics and Informatics.",
             keywords = "PROCESSAMENTO DIGITAL DE IMAGENS, radar transportado pelo ar, 
                         experimento de posi{\c{c}}{\~a}o e identifica{\c{c}}{\~a}o de 
                         pontos importantes, cobertura da terra, classifica{\c{c}}{\~a}o 
                         de imagens, reconhecimentos de padr{\~o}es, airborne radar, 
                         feature identification and location expeiment, land cover, image 
                         classification, pattern recognition.",
             abstract = "We study feature selection and classification for a land-cover 
                         mapping task from airborne high resolution AeS- 1 data in radar X- 
                         and P-Band. The studied feature selection methods are the 
                         well-established sequential addition of features to an initially 
                         empty feature set and genetic algorithm search, both based on 
                         statistical distance measures, and exhaustive evaluation based on 
                         actual classifier performance. It was observed, that different 
                         criteria and search strategies come up with different subsets of 
                         features. We present results of combined classifications derived 
                         from classifiers trained on different subsets of features. The 
                         considered combination strategies are product, sum, maximum and 
                         majority rules. Combination turned out to bring significant 
                         improvement. The task of discriminating two forest classes, three 
                         classes of agricultural area, two classes of built-up area and a 
                         specific class devoted to radar imaging ambiguities for a test 
                         site located in Switzerland provided a satisfying result for 
                         machine classification from radar data.",
  conference-location = "Orlando, EU",
      conference-year = "July 2000",
                label = "9468",
           targetfile = "2000_huber.pdf",
        urlaccessdate = "21 jan. 2021"
}


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