@Article{SotheAlScLiCuBeFe:2020:CoMaDe,
               author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Schimalski, 
                         Marcos Benedito and Liesenberg, Veraldo and Cue, Laura Elena and 
                         Bermudez, Jos{\'e} David and Feitosa, Raul Queiroz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade do 
                         Estado de Santa Catarina (UDESC)} 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 {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)}",
                title = "A comparison of machine and deep learning algorithms applied to 
                         multisource data for a subtropical forest area classification",
              journal = "International Journal of Remote Sensing",
                 year = "2020",
               volume = "41",
               number = "5",
                pages = "1943--1969",
                 note = "Setores de Atividade: Atividades dos servi{\c{c}}os de tecnologia 
                         da informa{\c{c}}{\~a}o, Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico, Produ{\c{c}}{\~a}o Florestal.",
             keywords = "forest succession stages, endangered tree species, convolutional 
                         neural networks, ensemble methods, light detection and ranging, 
                         multispectral data.",
             abstract = "This work explores the integration of airborne Light Detection and 
                         Ranging (LiDAR) data and WorldView-2 (WV2) images to classify the 
                         land cover of a subtropical forest area in Southern Brazil. 
                         Different deep and machine learning methods were used: one based 
                         on convolutional neural network (CNN) and three ensemble methods. 
                         We adopted both pixel- (in the case of CNN) and object-based 
                         approaches. The results demonstrated that the integration of LiDAR 
                         and WV2 data led to a significant increase (7% to 16%) in 
                         accuracies for all classifiers, with kappa coefficient (\κ) 
                         ranging from 0.74 for the random forest (RF) classifier associated 
                         with the WV2 dataset, to 0.92 for the forest by penalizing 
                         attributes (FPA) with the full (LiDAR + WV2) dataset. Using the 
                         WV2 dataset solely, the best \κ was 0.81 with CNN 
                         classifier, while for the LiDAR dataset, the best \κ was 0.8 
                         with the rotation forest (RotF) algorithm. The use of LiDAR data 
                         was especially useful for the discrimination of vegetation classes 
                         because of the different height properties among them. In its 
                         turn, the WV2 data provided better performance for classes with 
                         less structure variation, such as field and bare soil. All the 
                         classification algorithms had a nearly similar performance: the 
                         results vary slightly according to the dataset used and none of 
                         the methods achieved the best accuracy for all classes. It was 
                         noticed that both datasets (WV2 and LiDAR) even when applied alone 
                         achieved good results with deep and machine learning methods. 
                         However, the advantages of integrating active and passive sensors 
                         were evident. All these methods provided promising results for 
                         land cover classification experiments of the study area in this 
                         work.",
                  doi = "10.1080/01431161.2019.1681600",
                  url = "http://dx.doi.org/10.1080/01431161.2019.1681600",
                 issn = "0143-1161",
                label = "lattes: 1861914973833506 2 S{\"o}theAlScLiRoBeFe:2019:CoMaDe",
             language = "en",
           targetfile = "sothe_comparison.pdf",
                  url = "https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1681600",
        urlaccessdate = "28 abr. 2024"
}