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A novel approach for vegetation classification using UAV-based hyperspectral imaging

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Title: A novel approach for vegetation classification using UAV-based hyperspectral imaging
Authors: Ishida, Tetsuro Browse this author
Kurihara, Junichi Browse this author
Angelico Viray, Fra Browse this author
Baes Namuco, Shielo Browse this author
Paringit, Enrico C. Browse this author
Jane Perez, Gay Browse this author
Takahashi, Yukihiro Browse this author
Joseph Marciano, Joel, Jr. Browse this author
Keywords: Liquid crystal tunable filter
Unmanned aerial vehicle
Vegetation classification
Machine learning
Issue Date: Jan-2018
Publisher: Elsevier
Journal Title: Computers and electronics in agriculture
Volume: 144
Start Page: 80
End Page: 85
Publisher DOI: 10.1016/j.compag.2017.11.027
Abstract: The use of unmanned aerial vehicle (UAV)-based spectral imaging offers considerable advantages in high-resolution remote-sensing applications. However, the number of sensors mountable on a UAV is limited, and selecting the optimal combination of spectral bands is complex but crucial for conventional UAV-based multi spectral imaging systems. To overcome these limitations, we adopted a liquid crystal tunable filter (LCTF), which can transmit selected wavelengths without the need to exchange optical filters. For calibration and validation of the LCTF-based hyperspectral imaging system, a field campaign was conducted in the Philippines during March 28-April 3, 2016. In this campaign, UAV-based hyperspectral imaging was performed in several vegetated areas, and the spectral reflectances of 14 different ground objects were measured. Additionally, the machine learning (ML) approach using a support vector machine (SVM) model was applied to the obtained dataset, and a high resolution classification map was then produced from the aerial hyperspectral images. The results clearly showed that a large amount of misclassification occurred in shaded areas due to the difference in spectral reflectance between sunlit and shaded areas. It was also found that the classification accuracy was drastically improved by training the SVM model with both sunlit and shaded spectral data. As a result, we achieved a classification accuracy of 94.5% in vegetated areas.
Type: article (author version)
Appears in Collections:理学院・理学研究院 (Graduate School of Science / Faculty of Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 石田 哲朗

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