Abstract
Infrared ship-target detection for sea surveillance from the coast is very challenging because of strong background clutter, such as cloud and sea glint. Conventional approaches utilize either spatial or temporal information to reduce false positives. This paper proposes a completely different approach, called carbon dioxide-double spike (CO2-DS) detection in midwave spectral imaging. The proposed CO2-DS is based on the spectral feature where a hot CO2 emission band is broader than that which is absorbed by normal atmospheric CO2, which generates CO2-double spikes. A directional-mean subtraction filter (D-MSF) detects each CO2 spike, and final targets are detected by joint analysis of both types of detection. The most important property of CO2-DS detection is that it generates an extremely low number of false positive caused by background clutter. Only the hot CO2 spike of a ship plume can penetrate atmosphere, and furthermore, there are only ship CO2 plume signatures in the double spikes of different spectral bands. Experimental results using midwave Fourier transform infrared (FTIR) in a remote sea environment validate the extreme robustness of the proposed ship-target detection.
Highlights
Fourier transform infrared (FTIR) in a remote sea environment validate the extreme robustness of the proposed ship-target detection
Remote-ship detection is important in various applications, such as maritime navigation [1], coast guard searches [2], and homeland security procedures [3]
Remote-ship detection is important in various applications, such as navigation and surveillance
Summary
Remote-ship detection is important in various applications, such as maritime navigation [1], coast guard searches [2], and homeland security procedures [3]. Since the 1990s, a lot of methods have been proposed to reduce the false positives by using either spatial information or temporal information from infrared images Spatial information processing, such as background subtraction, can be a feasible approach to reducing background clutter. Spatial multi-feature fusion can increase the clutter discrimination capability [24] Temporal information processing, such as track-before-detection, can remove slowly moving cloud clutter and fast-blinking sea glint [25]. The temporal filter based approach assumes fast target motion with stationary sensors If this assumption is not satisfied, it will generate many false positives in the maritime environment. This paper proposes a novel spectral–spatial signature analysis-based ship detection method in midwave hyperspectral images, instead of the conventional spatial or temporal methods.
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