@InProceedings{NitzeSchuAsch:2012:CoMaLe,
               author = "Nitze, Ingmar and Schulthess, Urs and Asche, Hartmut",
                title = "Comparison of machine learning algorithms Random Forest, 
                         Artificial Neural Network and Support Vector Machine to Maximum 
                         Likelihood for supervised crop type classification",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "35--40",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Crop Classification, Machine Learning Algorithms, Support Vector 
                         Machine, RapidEye.",
             abstract = "The classification and recognition of agricultural crop types is 
                         an important application of remote sensing. New machine learning 
                         algorithms have emerged in the last years, but so far, few studies 
                         only have compared their performance and usability. Therefore, we 
                         compared three different state-of-the-art machine learning 
                         classifiers, namely Support Vector Machine (SVM), Artificial 
                         Neural Network (ANN) and Random Forest (RF) as well as the 
                         traditional classification method Maximum Likelihood (ML) among 
                         each other. For this purpose we classified a dataset of more than 
                         500 crop fields located in the Canadian Prairies with a stratified 
                         randomized sampling approach. Up to four multi-spectral RapidEye 
                         images from the 2009 growing season were used. We compared the 
                         mean overall classification accuracies as well as standard 
                         deviations. Furthermore, the classification accuracy of single 
                         crops was analysed. Support Vector Machine classifiers using 
                         radial basis function or polynomial kernels exhibited superior 
                         results to ANN and RF in terms of overall accuracy and robustness, 
                         while ML exhibited inferior accuracies and higher variability. 
                         Grassland exhibited the best results for early-season 
                         mono-temporal analysis. With a multi-temporal approach, the 
                         highest accuracies were achieved for Rapeseed and Field Peas. 
                         Other crops, such as Wheat, Flax and Lentils were also 
                         successfully classified. The users and producers accuracies were 
                         higher than 85 %.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BT2AF2",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BT2AF2",
           targetfile = "015.pdf",
                 type = "Classification",
        urlaccessdate = "10 maio 2024"
}