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A Robust Directional Saliency-Based Method for Infrared Small-Target Detection Under Various Complex Backgrounds

机译:复杂背景下基于鲁棒定向显着性的红外小目标检测方法

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Infrared small-target detection plays an important role in image processing for infrared remote sensing. In this letter, different from traditional algorithms, we formulate this problem as salient region detection, which is inspired by the fact that a small target can often attract attention of human eyes in infrared images. This visual effect arises from the discrepancy that a small target resembles isotropic Gaussian-like shape due to the optics point spread function of the thermal imaging system at a long distance, whereas background clutters are generally local orientational. Based on this observation, a new robust directional saliency-based method is proposed incorporating with visual attention theory for infrared small-target detection. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods for real infrared images with various typical complex backgrounds.
机译:红外小目标检测在红外遥感图像处理中起着重要作用。在这封信中,与传统算法不同,我们将此问题表述为显着区域检测,这是受以下事实启发的:小目标通常可以在红外图像中吸引人眼的注意力。这种视觉效果源于差异,即由于热成像系统的光学点扩展功能,小目标类似于各向同性的高斯形状,而背景杂波通常是局部定向的。在此基础上,结合视觉注意理论,提出了一种基于鲁棒定向显着性的红外小目标检测方法。实验结果表明,该算法优于具有各种典型复杂背景的真实红外图像的最新方法。

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