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Remote Sens. 2018, 10(8), 1319; https://doi.org/10.3390/rs10081319

Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery

1
Remote Sensing Technology and Data Center, Indonesian National Institute of Aeronautics and Space (Lembaga Penerbangan dan Antariksa Nasional, LAPAN), Jakarta 13710, Indonesia
2
Departement of Soil Science, Faculty of Agriculture, Hasanudin University, Makassar 90245, Indonesia
3
Faculty of Agriculture and Marine Science, Kochi University, Kochi 783-8502, Japan
4
Research Institute for Humanity and Nature, Kyoto 603-8047, Japan
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species. View Full-Text
Keywords: sago palm; OBIA; machine learning; textural features; image segmentation; feature selection; classification sago palm; OBIA; machine learning; textural features; image segmentation; feature selection; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hidayat, S.; MATSUOKA, M.; Baja, S.; Rampisela, D.A. Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery. Remote Sens. 2018, 10, 1319.

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