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

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.