Infrared Small Target Detection Using Modified Order Morphology and Weighted Local Entropy
Xiaocui Zhang, Jiannan Chi, Jingyao Hu, Linna Liu, Yongyue Xing
Available Online July 2016.
- https://doi.org/10.2991/iccia-17.2017.61How to use a DOI?
- Complex sky background, infrared image, small target detection, order morphology, local entropy.
- Infrared images in the sky scence often contain different complexity noises, which affect the validity of target detection algorithm and result in high false alarm rate. In order to solve this problem, this paper proposes a method based on weighted order morphology, which utilizes the improved open operation to do the denoising and then uses the edge structuring element to extract the small target, and defines a weighted order morphology-based top-hat (WOTH) operation to suppress background while enhancing the target, and finally complete the detection of target by using a simple adaptive threshold. The algorithm combines three different structuring elements, and has four adjustable parameters. By controlling the structure elements' size and related parameters, the target and the background can be quickly separated, the use of weighted local entropy operation also enhance the target effectively. The experimental results show that the proposed algorithm can effectively suppress different types of interference and accurately detect infrared small targets, and has a good performance in robustness and fastness.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xiaocui Zhang AU - Jiannan Chi AU - Jingyao Hu AU - Linna Liu AU - Yongyue Xing PY - 2016/07 DA - 2016/07 TI - Infrared Small Target Detection Using Modified Order Morphology and Weighted Local Entropy BT - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 356 EP - 365 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.61 DO - https://doi.org/10.2991/iccia-17.2017.61 ID - Zhang2016/07 ER -