@InProceedings{ArvorSaiDupAndDur:2013:IdOpCl,
               author = "Arvor, Damien and Saint-Geours, Nathalie and Dupuy, St{\'e}phane 
                         and Andr{\'e}s, Samuel and Durieux, Laurent",
                title = "Identifying optimal classification rules for geographic 
                         object-based image analysis",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2290--2297",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In Geographic Object-based Image Analysis (GEOBIA), remote sensing 
                         experts benefit from a large spectrum of characteristics to 
                         interpret images (spectral information, texture, geometry, spatial 
                         relations, etc). However, the quality of a classification is not 
                         always increased by considering a higher number of features. The 
                         experts are then used to define classification rules based on a 
                         laborious {"}trial-and-error{"} process. In this paper, we test a 
                         methodology to automatically determine an optimal subset of 
                         features for discriminating features. This method assumes that a 
                         reference land cover map (or at least training samples) is 
                         available. Two approaches were considered: a rule-based approach 
                         and a Support Vector Machine approach. For each approach, the 
                         method consists in ranking the features according to their 
                         potential for discriminating two classes. This task was performed 
                         thanks to the Jeffries-Matusita distance and Support Vector 
                         Machine-Ranking Feature Extraction (SVM-RFE) algorithm. Then, it 
                         consists in training and validating a classification algorithm 
                         (rule-based and SVM), with an increasing number of features: first 
                         only the best-ranked feature is included in the classifier, then 
                         the two best-ranked features, etc., until all the N features are 
                         included. The objective is to analyze how the quality of the 
                         classification evolves according to the numbers of features used. 
                         The optimal subset of features is finally determined through the 
                         analysis of the Akaike information criterion. The methodology was 
                         tested on two classes (urban an non urban areas) on a Spot5 image 
                         regarding a study area located in the La R{\'e}union island.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "1605",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GMB5",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GMB5",
           targetfile = "p1605.pdf",
                 type = "Classifica{\c{c}}{\~a}o e Minera{\c{c}}{\~a}o de Dados",
        urlaccessdate = "12 maio 2024"
}