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18 December 2019 Infrared small target detection algorithm based on multi-directional derivative and local contrast
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Proceedings Volume 11338, AOPC 2019: Optical Sensing and Imaging Technology; 1133825 (2019) https://doi.org/10.1117/12.2547279
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China
Abstract
Infrared small target detection technology plays an extremely important role in military remote warning, satellite remote sensing technology, guidance and anti-mission, UAV detection and tracking, and other fields. Most traditional algorithms have high detection false alarm when there are thin and highlighted patterns in the background. To address the abovediscussed problem, this paper proposes an infrared small target detection algorithm based on multi-direction derivative and local contrast. The algorithm utilizes the two-dimensional Gaussian distribution of small targets to compute multidirectional derivative on each pixel of the image. Simultaneously, a sliding window is constructed to compute the local contrast. Finally, the derivative result and the local contrast is combined to get the target saliency map. Compared with traditional infrared small target detection algorithms in terms of background suppression factor (BSF) and signal-to-clutter ratio (SCR), our algorithms has better performance in both indicators. In addition, Receive Operating Characteristic (ROC) curve is introduced to evaluate the performance of the algorithm. The curve demonstrates that the proposed algorithm can achieve high detection rate with low false alarm rate preserved. The experimental results show that the proposed algorithm is simple, efficient and has high detection accuracy.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weixi Liu, Xiangyong Meng, Weixian Qian, Minjie Wan, and Qian Chen "Infrared small target detection algorithm based on multi-directional derivative and local contrast", Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 1133825 (18 December 2019); https://doi.org/10.1117/12.2547279
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