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Proceedings Paper

Novel metrics for object discrimination in overhead real and synthetic color images
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Paper Abstract

The evaluation and discrimination of similar objects in real versus synthetically generated aerial color images is needed for security and surveillance purposes among other applications. Identification of appropriate discrimination metrics between real versus synthetic images may also help in more robust generation of these synthetic images. In this paper, we investigate the effectiveness of three different metrics based on Gaussian Blur, Differential Operators and singular value decomposition (SVD) to differentiate between a pair of same objects contained in real and synthetic overhead aerial color images. We use nine pairs of images in our tests. The real images were obtained in the visible aerial color image domain. The proposed metrics are used to discriminate between pairs of real and synthetic objects such as cooling units, industrial buildings, houses, conveyors, stacks, piles, railroads and ponds in these real and synthetically generated images respectively. The proposed method successfully discriminates between the real and synthetic objects in aerial color images without any apriori knowledge or extra information such as optical flow. We ranked these metrics according to their effectiveness to discriminate between synthetic and real objects in overhead images.

Paper Details

Date Published: 13 June 2014
PDF: 7 pages
Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908810 (13 June 2014); doi: 10.1117/12.2052887
Show Author Affiliations
Prakash Duraisamy, Old Dominion Univ. (United States)
Amr H. Yousef, Old Dominion Univ. (United States)
Khan M. Iftekharuddin, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 9088:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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