@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 = "27 abr. 2024"
}