A Multiscale Fuzzy Metric for Detecting Small Infrared Targets Against Chaotic Cloudy/Sea-Sky Backgrounds

He Deng, Xianping Sun, Xin Zhou

Abstract: In a low signal-to-clutter ratio (SCR) small-infrared-target image with chaotic cloudy-/sea-sky background, the target has very similar thermal intensities to the background (e.g., edges of clouds). In such case, how to accurately detect small targets is crucial in infrared search and tracking applications. Conventional methods based on the local difference/mutation potentially result in high miss and/or false alarm rates. Here, we propose an effective method for detecting small infrared targets embedded in complex backgrounds through a multiscale fuzzy metric that measures the certainty of targets in images. Accordingly, the detection task is formulated as a fuzzy measure issue. The presented metric is able to eliminate substantial background clutters and noise. Especially, it significantly improves SCR values of the image. Subsequently, a simple and adaptive threshold is used to segment target. Extensive clipped and real data experiments demonstrate that the proposed algorithm not only works more robustly for different target sizes, SCR values, target and/or background types, but also has better performance regarding detection accuracy, when compared with traditional baseline methods. Moreover, the mathematical proofs are provided for understanding the proposed detection method.