Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging,search-and-rescue and perimeter or harbour defense. IR video was chosen for its day-and-night imaging capability, and its availability on military vessels. Detection is difficult because a rough sea is seen as a dynamic background of moving objects with size order, shape and temperature similar to those of the floating mine. We do find a determinant characteristic in the target's periodic motion, which differs from that of the propagating surface waves composing the background. The classical detection and tracking approaches give bad results when applied to this problem. While background detection algorithms assume a quasi-static background, the sea surface is actually very dynamic, causing this category of algorithms to fail. Kalman or particle filter algorithms on the other hand, which stress temporal coherence, suffer from tracking loss due to occlusions and the great noise level of the image. We propose an innovative approach. This approach uses the periodicity of the objects movement and thus its temporal coherence. The principle is to consider the video data as a spacetime volume similar to a hyperspectral data cube by replacing the spectral axis with a temporal axis. We can then apply algorithms developed for hyperspectral detection problems to the detection of small floating objects. We treat the detection problem using multilinear algebra, designing a number of finite impulse response filters (FIR) maximizing the target response. The algorithm was applied to test footage of practice mines in the infrared.

Date Published: 30 September 2009
PDF: 11 pages
Proc. SPIE 7482, Electro-Optical Remote Sensing, Photonic Technologies, and Applications III, 74820U (30 September 2009); doi: 10.1117/12.830224

Published in SPIE Proceedings Vol. 7482:
Electro-Optical Remote Sensing, Photonic Technologies, and Applications III
Gary J. Bishop; Keith L. Lewis; Gary W. Kamerman; John D. Gonglewski; Richard C. Hollins; Ove K. Steinvall; Thomas J. Merlet, Editor(s)