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ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Evaluation of Automatic Shadow Detection Approaches Using ADS-40 High Radiometric Resolution Aerial Images at High Mountainous Region

Jan-Chang C1*, Yi-Ta H1, Chaur-Tzuhn C1 and Shou-Tsung W2

1Department of Forestry, National Pingtung University of Science and Technology, Pingtung, Taiwan

2Department of Tourism Management, Shih Chien University, Kaohsiung, Taiwan

*Corresponding Author:
Jan-Chang C
Department of Forestry
National Pingtung University of Science and Technology
No. 1, Shuehfu Road, Neipu
Pingtung, Taiwan
Tel: 88687703202
E-mail: [email protected]

Received date: April 22, 2016; Accepted date: May 18, 2016; Published date: May 25, 2016

Citation: Jan-Chang C, Yi-Ta H, Chaur-Tzuhn C, Shou-Tsung W (2016) Evaluation of Automatic Shadow Detection Approaches Using ADS-40 High Radiometric Resolution Aerial Images at High Mountainous Region. J Remote Sensing & GIS 5: 165. doi: 10.4172/2469-4134.1000165

Copyright: © 2016 Jan-Chang C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Shadow detection is a very important pre-processing step for remote sensing applications, particularly for high spatial resolution data. This study adopts the high resolution aerial images to investigate the shadow detection issue. Testing three shadow detection methods (Brightness method, Nagao’s method, NIR method), and look for a suitable shadow detection method for high radiometric resolution aerial images. We also discuss the differences for each method. The results indicate Brightness method and Nagao’s method are significantly better than NIR method. NIR often confuses water bodies with shadows due to the low reflectance of water. The data space we used, Nagao’s modified intensity and brightness are better than NIR channel to discriminate shadows from non-shadows, and the two methods are both not easily confused with shadow and water bodies. In our study, we found that most of cases presented shadow in the histogram of first mode, and the first valley detection thresholding is a robust way to detect the shadow threshold of histogram. A good data space and defining the optimum thresholding method will affect histogram thresholding result.

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