Automatic ship detection from commercial multispectral satellite imagery

Brian J. Daniel; Alan P. Schaum; Eric C. Allman; Robert A. Leathers; Trijntje V. Downes

doi:10.1117/12.2017762

18 May 2013 Automatic ship detection from commercial multispectral satellite imagery

Brian J. Daniel, Alan P. Schaum, Eric C. Allman, Robert A. Leathers, Trijntje V. Downes

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 874312 (2013) https://doi.org/10.1117/12.2017762
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Commercial multispectral satellite sensors spend much of their time over the oceans. NRL has demonstrated an automatic processing system for finding ships at sea using commercially available multispectral data. To distinguish ships from whitecaps and clouds, a water/cloud clutter subspace is estimated and a continuum fusion derived anomaly detection algorithm is applied. This provides a maritime awareness capability with an acceptable detection rate while maintaining a low rate of false alarms. The system also provides a confidence metric, which can be used to further limit the false alarm rate.

Citation Download Citation

Brian J. Daniel, Alan P. Schaum, Eric C. Allman, Robert A. Leathers, and Trijntje V. Downes "Automatic ship detection from commercial multispectral satellite imagery", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874312 (18 May 2013); https://doi.org/10.1117/12.2017762

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