author = "Moreno, Ram{\'o}n and Corona, Francesco and Lendasse, Amaury and 
                         Graņa, Manuel and Galv{\~a}o, L{\^e}nio Soares",
          affiliation = "{Universidad del Pa{\'{\i}}s Vasco} and {Aalto University} and 
                         {Aalto University} and {Universidad del Pa{\'{\i}}s Vasco} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Extreme learning machines for soybean classification in remote 
                         sensing hyperspectral images",
              journal = "Neurocomputing",
                 year = "2014",
               volume = "128",
                pages = "207--216",
             keywords = "Agricultural remote sensing, Classification performance, 
                         Classification process, Extreme learning machine, Feed-forward 
                         network, Functional data analysis, Hyper-spectral images, 
                         State-of-the-art algorithms, Algorithms, Crops, Knowledge 
                         acquisition, Maps, Network layers, Pixels, Remote sensing, 
                         Spectroscopy, Classification (of information), accuracy, 
                         agricultural land, algorithm, article, Brazil, classification, 
                         crop, extreme learning machine, image analysis, linear system, 
                         machine learning, mathematical computing, mathematical model, 
                         nonlinear system, priority journal, remote sensing, signal noise 
                         ratio, soybean.",
             abstract = "This paper focuses on the application of Extreme Learning Machines 
                         (ELM) to the classification of remote sensing hyperspectral data. 
                         The specific aim of the work is to obtain accurate thematic maps 
                         of soybean crops, which have proven to be difficult to identify by 
                         automated procedures. The classification process carried out is as 
                         follows: First, spectral data is transformed into a 
                         hyper-spherical representation. Second, a robust image gradient is 
                         computed over the hyper-spherical representation allowing an image 
                         segmentation that identifies major crop plots. Third, feature 
                         selection is achieved by a greedy wrapper approach. Finally, a 
                         classifier is trained and tested on the selected image pixel 
                         features. The classifiers used for feature selection and final 
                         classification are Single Layer Feedforward Networks (SLFN) 
                         trained with either the ELM or the incremental OP-ELM. Original 
                         image pixel features are computed following a Functional Data 
                         Analysis (FDA) characterization of the spectral data. Conventional 
                         ELM training of the SLFN improves over the classification 
                         performance of state of the art algorithms reported in the 
                         literature dealing with the data treated in this paper. Moreover, 
                         SLFN-ELM uses less features than the referred algorithms. OP-ELM 
                         is able to find competitive results using the FDA features from a 
                         single spectral band.",
                  doi = "10.1016/j.neucom.2013.03.057",
                  url = "http://dx.doi.org/10.1016/j.neucom.2013.03.057",
                 issn = "0925-2312 and 1872-8286",
                label = "scopus 2014-05 MorenoCorLenGraGal:2014:ExLeMa",
             language = "en",
           targetfile = "1-s2.0-S0925231213010102-main.pdf",
        urlaccessdate = "04 dez. 2020"