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Remote Sens. 2016, 8(6), 445; doi:10.3390/rs8060445

Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

1
Romberg Tiburon Center for Environment Studies, San Francisco State University, 3150 Paradise Dr., Tiburon, CA 94920, USA
2
Geography and Environment, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA
3
Kruse Imaging, 3230 Ross Road, Palo Alto, CA 94303, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Anu Swatantran, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 24 March 2016 / Revised: 6 May 2016 / Accepted: 18 May 2016 / Published: 24 May 2016
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Abstract

The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier. View Full-Text
Keywords: hyperspectral imagery; tree species classification; support vector machine; random forest hyperspectral imagery; tree species classification; support vector machine; random forest
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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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MDPI and ACS Style

Ballanti, L.; Blesius, L.; Hines, E.; Kruse, B. Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers. Remote Sens. 2016, 8, 445.

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