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A Novel Spectral Index for Automatic Shadow Detection in Urban Mapping Based On WorldView-2 Satellite Imagery

Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

Abstract:

In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of WorldView-2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows with accuracy of 94% effectively and automatically. Furthermore, the new shadow detection index improved road extraction from 82% to 93%.

Keywords: spectral index, shadow detection, WorldView-2, remote sensing images

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096195

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References:


[1] P. M. Dare, "Shadow analysis in high-resolution satellite imagery of urban areas." Photogrammetric Eng. & Remote Sens.71, no. 2, 2005, pp. 169-177.
[2] A.K. Saha, M. K. Arora, E. Csaplovics, and R. P. Gupta. "Land cover classification using IRS LISS III image and DEM in a rugged terrain: a case study in Himalayas." Geocarto Int. 20, no. 2, 2005, pp. 33-40.
[3] V. Arévalo, J. González, and G. Ambrosio. "Detecting shadows in QuickBird satellite images." In ISPRS Commission VII Mid-term Symposium'Remote Sens: From Pixels to Processes. 2005.
[4] P. Sarabandi, F. Yamazaki, M. Matsuoka, and A. Kiremidjian. "Shadow detection and radiometric restoration in satellite high resolution images." Proc. IEEE of IGARSS-2004, September 2004, Anchorage, Alaska, New York, CDROM 2004.
[5] Y. Chen, D. Wen, L. Jing, and P. Shi. "Shadow information recovery in urban areas from very high resolution satellite imagery." Int. J. of Remote Sens. 28, no. 15, 2007, pp.3249-3254.
[6] T. Gustav, M. Shimoni, and J. Ahlberg. "A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data." In IEEE Int. Geosci. and Remote Sens. Symposium (IGARSS), 2011, pp. 4423-4426.
[7] A. Lizy, and M. Sasikumar. "An efficient shadow detection method for high resolution satellite images." (Published Conference Proceedings style) IEEE In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on, 2012, pp. 1-5.
[8] K.R.M Adeline, M. Chen, X. Briottet, S. K. Pang, and N. Paparoditis. "Shadow detection in very high spatial resolution aerial images: A comparative study." ISPRS Journal of Photogrammetry and Remote Sens. 80, 2013, pp. 21-38.
[9] V. Arévalo, J. González, and G. Ambrosio. "Shadow detection in colour high‐resolution satellite images." Int. J. of Remote Sens 29, no. 7, 2008, pp.1945-1963.
[10] L. Hégarat-Mascle, and C. André. "Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images." ISPRS J. of Photogrammetry and Remote Sens. 64, no. 4, 2009, pp.351-366.
[11] L. Wen, and F. Yamazaki. "Object-based shadow extraction and correction of high-resolution optical satellite images." IEEE J. of Select. Topics in Appl. Earth Observations and Remote Sens. no. 4, 2012, pp.1296-1302.
[12] L. Jiahang, T. Fang, and D. Li. "Shadow detection in remotely sensed images based on self-adaptive feature selection." Geoscience and Remote Sensing, IEEE Trans. on 49, no. 12, 2011, pp.5092-5103.
[13] P. Andrea, I. Mikic, M.M. Trivedi, and R. Cucchiara. "Detecting moving shadows: algorithms and evaluation." IEEE Trans. Pattern Anal. Machine Intel. 25, no. 7, 2003, pp.918-923.
[14] X. Li, F. Qi, R. Jiang, Y. Hao, G. Wu, L. Xu, F. Qi, R. Jiang, Y. Hao, and G. Wu. "Shadow detection and removal in real images: a survey." (Unpublished work style) Computer Vision Lab, Dept. of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai (2006).
[15] DigitalGlobe. White Paper: The Benefits of the 8 Spectral Bands of WorldView–2. Longmont, CO: DigitalGlobe 2009.