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Proceedings Paper

Ship detection in satellite imagery using rank-order grayscale hit-or-miss transforms
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Paper Abstract

Ship detection from satellite imagery is something that has great utility in various communities. Knowing where ships are and their types provides useful intelligence information. However, detecting and recognizing ships is a difficult problem. Existing techniques suffer from too many false-alarms. We describe approaches we have taken in trying to build ship detection algorithms that have reduced false alarms. Our approach uses a version of the grayscale morphological Hit-or-Miss transform. While this is well known and used in its standard form, we use a version in which we use a rank-order selection for the dilation and erosion parts of the transform, instead of the standard maximum and minimum operators. This provides some slack in the fitting that the algorithm employs and provides a method for tuning the algorithm's performance for particular detection problems. We describe our algorithms, show the effect of the rank-order parameter on the algorithm's performance and illustrate the use of this approach for real ship detection problems with panchromatic satellite imagery.

Paper Details

Date Published: 15 April 2010
PDF: 12 pages
Proc. SPIE 7701, Visual Information Processing XIX, 770102 (15 April 2010); doi: 10.1117/12.850886
Show Author Affiliations
Neal R. Harvey, Los Alamos National Lab. (United States)
Reid Porter, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7701:
Visual Information Processing XIX
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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