Infrared Small-Target Detection Using Multidirectional Local Difference Measure Weighted by Entropy

Huang Yao, Liping Liu, Yantao Wei (), Di Chen and Mingwen Tong
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Huang Yao: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Liping Liu: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Yantao Wei: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Di Chen: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Mingwen Tong: Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China

Sustainability, 2023, vol. 15, issue 3, 1-13

Abstract: Detecting small targets from infrared remote sensing images is still a challenging task. In this research, we propose a multidirectional local difference measure weighted by entropy (MDLDE) to detect small targets from infrared images with messy backgrounds. First, a new multidirectional local difference measure is proposed to suppress the clutter background. Then, the entropy, which captures the overall heterogeneity between the target and the background, is utilized to enhance the target. Lastly, an adaptive threshold was adopted to segment the target region from the background. The designed MDLDE could effectively enhance the target and simultaneously suppress the background clutter. Experimental results on six datasets indicate that the proposed method outperformed other state-of-the-art methods in terms of the signal-to-clutter ratio gain (SCRG), background suppression factor (BSF), and receiver operating characteristic (ROC) curves.

Keywords: small-target detection; infrared image; directional local difference measure; entropy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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