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Proceedings Paper

Segmenting Shadows from synthetic aperture radar imagery using edge-enhanced region growing
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Paper Abstract

An enhanced region-growing approach for segmenting regions is introduced. A region-growing algorithm is merged with stopping criteria based on a robust noise-tolerant edge-detection routine. The region-grow algorithm is then used to segment the shadow region in a Synthetic Aperture Radar (SAR) image. This approach recognizes that SAR phenomenology causes speckle in imagery even to the shadow area due to energy injected from the surrounding clutter and target. The speckled image makes determination of edges a difficult task even for the human observer. This paper outlines the edge-enhanced region grow approach and compares the results to three other segmentation approaches including the region-grow only approach, an automated-threshold approach based on a priori knowledge of the SAR target information, and the manual segmentation approach. The comparison is shown using a tri-metric inter- algorithmic approach. The metrics used to evaluate the segmentation include percent-pixels same (PPS), the partial- directed hausdorff (PDH) metric, and a shape-based metric based on the complex inner product (CIP). Experimental results indicate that the enhanced region-growing technique is a reasonable segmentation for the SAR target image chips obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program.

Paper Details

Date Published: 2 November 2000
PDF: 9 pages
Proc. SPIE 4113, Algorithms and Systems for Optical Information Processing IV, (2 November 2000); doi: 10.1117/12.405840
Show Author Affiliations
Gregory J. Power, Air Force Research Lab. (United States)
Kelce S. Wilson, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4113:
Algorithms and Systems for Optical Information Processing IV
Bahram Javidi; Demetri Psaltis, Editor(s)

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