Detecting and Counting Coconut Trees in Pleiades Satellite Imagery using Histogram of Oriented Gradients and Support Vector Machine

Yudhi Prabowo and Kenlo Nishida Nasahara (2019) Detecting and Counting Coconut Trees in Pleiades Satellite Imagery using Histogram of Oriented Gradients and Support Vector Machine. International Journal of Remote Sensing and Earth Sciences, 16 (1). pp. 87-98. ISSN 0216-6739

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: coconut trees, Pleiades imagery, tree detection, histogram of oriented gradient, support vector machine
Subjects: Teknologi Penginderaan Jauh > Pengelolaan dan Pengembangan > Citra Satelit
Divisions: Deputi Penginderaan Jauh > Pusat Teknologi dan Data Penginderaan Jauh
Depositing User: Dinar Indrasasi
Date Deposited: 27 Jul 2021 23:55
Last Modified: 27 Jul 2021 23:55
URI: http://repositori.lapan.go.id/id/eprint/1065

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