DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.30536/j.ijreses.2019.v16.a3089Keywords:
coconut trees, Pleiades imagery, tree detection, histogram of oriented gradient, support vector machineAbstract
This paper describes the detection of coconut trees using very-high-resolution optical satellite
imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a
spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients
(HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this
detection. The main objective of this study is to find out the parameter combination for the HOG
algorithm that could provide the best performance for coconut-tree detection. The study shows that the
best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins,
and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of
approximately 80%, 73% and 87%, respectively.
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