Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

作者：Kim Sungho^*

来源：Journal of Infrared Millimeter and Terahertz Waves, 2011, 32(1): 79-101.

DOI：10.1007/s10762-010-9742-9

• IRST
• Horizontal target
• Heterogeneous background estimation
• Target detection
• Adaptive hysteresis threshold

Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

• 出版日期2011-1