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@Article{SotheASLCFDLLMT:2020:CoPeCo,
               author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Schimalski, 
                         M. B. and La Rosa, L. E. C. and Castro, J. D. B. and Feitosa, R. 
                         Q. and Dalponte, M. and Lima, C. L. and Liesenberg, V. and Miyosh, 
                         G. T. and Tommaselli, A. M. G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Santa Catarina State 
                         University (UDESC)} and {Pontifical Catholic University of Rio de 
                         Janeiro (PUC)} and {Pontifical Catholic University of Rio de 
                         Janeiro (PUC)} and {Pontifical Catholic University of Rio de 
                         Janeiro (PUC)} and {Research and Innovation Centre} and {Santa 
                         Catarina State University (UDESC)} and {Santa Catarina State 
                         University (UDESC)} and {S{\~a}o Paulo State University (UNESP)} 
                         and {S{\~a}o Paulo State University (UNESP)}",
                title = "Comparative performance of convolutional neural network, weighted 
                         and conventional support vector machine and random forest for 
                         classifying tree species using hyperspectral and photogrammetric 
                         data",
              journal = "GIScience and Remote Sensing",
                 year = "2020",
               volume = "57",
               number = "3",
                pages = "369--394",
                month = "apr.",
             keywords = "Tropical diversity, individual tree crown, deep learning, 
                         imbalanced sample set, unmanned aerial vehicle.",
             abstract = "The classification of tree species can significantly benefit from 
                         high spatial and spectral information acquired by unmanned aerial 
                         vehicles (UAVs) associated with advanced classification methods. 
                         This study investigated the following topics concerning the 
                         classification of 16 tree species in two subtropical forest 
                         fragments of Southern Brazil: i) the potential integration of 
                         UAV-borne hyperspectral images with 3D information derived from 
                         their photogrammetric point cloud (PPC); ii) the performance of 
                         two machine learning methods (support vector machine - SVM and 
                         random forest - RF) when employing different datasets at a pixel 
                         and individual tree crown (ITC) levels; iii) the potential of two 
                         methods for dealing with the imbalanced sample set problem: a new 
                         weighted SVM (wSVM) approach, which attributes different weights 
                         to each sample and class, and a deep learning classifier 
                         (convolutional neural network - CNN), associated with a previous 
                         step to balance the sample set; and finally, iv) the potential of 
                         this last classifier for tree species classification as compared 
                         to the above mentioned machine learning methods. Results showed 
                         that the inclusion of the PPC features to the hyperspectral data 
                         provided a great accuracy increase in tree species classification 
                         results when conventional machine learning methods were applied, 
                         between 13 and 17% depending on the classifier and the study area 
                         characteristics. When using the PPC features and the canopy height 
                         model (CHM), associated with the majority vote (MV) rule, the SVM, 
                         wSVM and RF classifiers reached accuracies similar to the CNN, 
                         which outperformed these classifiers for both areas when 
                         considering the pixel-based classifications (overall accuracy of 
                         84.4% in Area 1, and 74.95% in Area 2). The CNN was between 22% 
                         and 26% more accurate than the SVM and RF when only the 
                         hyperspectral bands were employed. The wSVM provided a slight 
                         increase in accuracy not only for some lesser represented classes, 
                         but also some major classes in Area 2. While conventional machine 
                         learning methods are faster, they demonstrated to be less stable 
                         to changes in datasets, depending on prior segmentation and 
                         hand-engineered features to reach similar accuracies to those 
                         attained by the CNN. To date, CNNs have been barely explored for 
                         the classification of tree species, and CNN-based classifications 
                         in the literature have not dealt with hyperspectral data 
                         specifically focusing on tropical environments. This paper thus 
                         presents innovative strategies for classifying tree species in 
                         subtropical forest areas at a refined legend level, integrating 
                         UAV-borne 2D hyperspectral and 3D photogrammetric data and relying 
                         on both deep and conventional machine learning approaches.",
                  doi = "10.1080/15481603.2020.1712102",
                  url = "http://dx.doi.org/10.1080/15481603.2020.1712102",
                 issn = "1548-1603",
             language = "en",
           targetfile = "sothe_comparative.pdf",
        urlaccessdate = "23 jan. 2021"
}


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