@Article{SötheLAGSCFDLLMT:2020:EvCoNe,
               author = "S{\"o}the, Camile and La Rosa, L. E. C. and Almeida, Cl{\'a}udia 
                         Maria de and Gonsamo, A. and Schimalski, Marcos Benedito and 
                         Castro, J. D. B. and Feitosa, Raul Queiroz and Dalponte, Michele 
                         and Lima, Carla Luciane and Liesenberg, Veraldo and Miyoshi, 
                         Gabriela Takahashi and Tommaselli, Antonio Maria Garcia",
          affiliation = "{McMaster University} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {McMaster University} and 
                         {Universidade do Estado de Santa Catarina (UDESC)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Fondazione Edmund Mach} and 
                         {Universidade do Estado de Santa Catarina (UDESC)} and 
                         {Universidade do Estado de Santa Catarina (UDESC)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Universidade 
                         Estadual Paulista (UNESP)}",
                title = "Evaluating a convolutional neural network for feature extraction 
                         and tree species classification using uav-hyperspectral images",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2020",
               volume = "3",
                pages = "193--199",
                 note = "Setores de Atividade: Atividades dos servi{\c{c}}os de tecnologia 
                         da informa{\c{c}}{\~a}o, Produ{\c{c}}{\~a}o Florestal, 
                         Pesquisa e desenvolvimento cient{\'{\i}}fico.",
             keywords = "Tropical diversity, unmanned aerial vehicle, deep learning, 
                         convolutional neural networks, support vector machine, data 
                         augmentation.",
             abstract = "The classification of tree species can significantly benefit from 
                         high spatial and spectral information acquired by unmanned aerial 
                         vehicles (UAVs) associated with advanced feature extraction and 
                         classification methods. Different from the traditional feature 
                         extraction methods, that highly depend on users knowledge, the 
                         convolutional neural network (CNN)-based method can automatically 
                         learn and extract the spatial-related features layer by layer. 
                         However, in order to capture significant features of the data, the 
                         CNN classifier requires a large number of training samples, which 
                         are hardly available when dealing with tree species in tropical 
                         forests. This study investigated the following topics concerning 
                         the classification of 14 tree species in a subtropical forest area 
                         of Southern Brazil: i) the performance of the CNN method 
                         associated with a previous step to increase and balance the sample 
                         set (data augmentation) for tree species classification as 
                         compared to the conventional machine learning methods support 
                         vector machine (SVM) and random forest (RF) using the original 
                         training data; ii) the performance of the SVM and RF classifiers 
                         when associated with a data augmentation step and spatial features 
                         extracted from a CNN. Results showed that the CNN classifier 
                         outperformed the conventional SVM and RF classifiers, reaching an 
                         overall accuracy (OA) of 84.37% and Kappa of 0.82. The SVM and RF 
                         had a poor accuracy with the original spectral bands (OA 62.67% 
                         and 59.24%) but presented an increase between 14% and 21% in OA 
                         when associated with a data augmentation and spatial features 
                         extracted from a CNN.",
                  doi = "10.5194/isprs-annals-v-3-2020-193-2020",
                  url = "http://dx.doi.org/10.5194/isprs-annals-v-3-2020-193-2020",
                 issn = "0924-2716",
                label = "lattes: 1861914973833506 3 S{\"o}theLAGSCFDLLMT:2020:EVCONE",
             language = "en",
           targetfile = "sothe_evaluating.pdf",
                  url = "http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/193/2020/",
        urlaccessdate = "27 abr. 2024"
}