Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

Autor: C. M. de Almeida, Jose David Bermudez Castro, Laura Elena Cué La Rosa, Carla Luciane Lima, Antonio Maria Garcia Tommaselli, Camile Sothe, Michele Dalponte, Raul Queiroz Feitosa, Marcos Benedito Schimalski, Gabriela Takahashi Miyoshi, Veraldo Liesenberg
Přispěvatelé: National Institute for Space Research (INPE), Santa Catarina State University (UDESC), Pontifical Catholic University of Rio de Janeiro (PUC), Research and Innovation Centre, Universidade Estadual Paulista (Unesp)
Rok vydání: 2020
Předmět:
Zdroj: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
ISSN: 1943-7226
1548-1603
Popis: Made available in DSpace on 2020-12-12T01:54:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-02 Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) 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. Division of Remote Sensing National Institute for Space Research (INPE) Department of Forest Engineering Santa Catarina State University (UDESC) Department of Electrical Engineering Pontifical Catholic University of Rio de Janeiro (PUC) Department of Sustainable Agro-Ecosystems and Bioresources Research and Innovation Centre Department of Geography Santa Catarina State University (UDESC) Department of Cartography São Paulo State University (UNESP) Department of Cartography São Paulo State University (UNESP) Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina: 2017TR1762 CNPq: 313887/2018-7 CAPES: 88882.330700/2018-01
Databáze: OpenAIRE
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