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

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 followi...

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Bibliographic Details
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
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Summary: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.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2021.3117085