IEEE Trans Cybern. 2019 May;49(5):1694-1707. doi: 10.1109/TCYB.2018.2810832. Epub 2018 Mar 6.
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.
在低信噪比（SCR）的小红外目标图像中，存在混沌的云和/天空背景，目标与背景（例如云的边缘）具有非常相似的热强度。在这种情况下，如何准确地检测小目标是红外搜索和跟踪应用中的关键。基于局部差异/突变的传统方法可能导致高漏报率和/或误报率。在这里，我们提出了一种通过多尺度模糊度量来检测复杂背景中嵌入的小红外目标的有效方法，该度量用于测量图像中目标的确定性。因此，检测任务被公式化为模糊度量问题。所提出的度量能够消除大量的背景杂波和噪声。特别是，它显著提高了图像的 SCR 值。随后，使用简单自适应的阈值来分割目标。广泛的剪辑和真实数据实验表明，与传统基线方法相比，所提出的算法不仅对不同的目标大小、SCR 值、目标和/或背景类型更稳健，而且在检测精度方面也具有更好的性能。此外，还提供了数学证明来理解所提出的检测方法。
