Entropy-Driven Morphological Top-Hat Transformation for Infrared Small Target Detection

• Published in: IEEE transactions on aerospace and electronic systems 2022-04, Vol.58 (2), p.962-975
• Main Authors: Deng, Lizhen, Xu, Guoxia, Zhang, Jieke, Zhu, Hu
• Format: Article
• Language: English
• Subjects: Algorithms; Clutter; Entropy; Entropy-Driven morphological method; Feature extraction; guided filter; Heat of transformation; infrared small target detection; Kernel; Kernels; local mean entropy; Morphology; Object detection; Shape; Structural members; Target detection; top-hat transformation
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2021.3117085

Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods.