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Proceedings Paper

Objective evaluation of four SAR image segmentation algorithms
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Paper Abstract

Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This paper evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentations. This objective comparison uses a multi-measure approach with a set of master segmentations as ground truth. The measure results are compared to a Human Threshold, which defines the performance of human segmentors compared to the master segmentations. Also, methods that use the multi-measures to determine the best algorithm are developed. These methods show that of the four algorithms, Statistical Curve Evolution produces the best segmentations; however, none of the algorithms are superior to human segmentations. Thus, with the Human Threshold and Statistical Curve Evolution as benchmarks, this paper establishes a new and practical framework for testing SAR image segmentation algorithms.

Paper Details

Date Published: 27 August 2001
PDF: 12 pages
Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438228
Show Author Affiliations
Jason B. Gregga, Air Force Research Lab. (United States)
Steven C. Gustafson, Air Force Institute of Technology (United States)
Gregory J. Power, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4382:
Algorithms for Synthetic Aperture Radar Imagery VIII
Edmund G. Zelnio, Editor(s)

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