Small Infrared Target Detection Based on Local Difference Adaptive Measure

• Published in: IEEE geoscience and remote sensing letters Vol. 17; no. 7; pp. 1258 - 1262
• Main Authors: Li, Lin, Li, Zhengzhou, Li, Yongsong, Chen, Cheng, Yu, Jiangpeng, Zhang, Chao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloudy-sky background; Complexity theory; Detection; Direction; Eigenvalues and eigenfunctions; Image edge detection; infrared image; Infrared imagery; local difference adaptive measure (LDAM); local structure tensor (LST); Methods; Object detection; Shape; small target detection; Target detection; Tensors; Uncertainty
• DOI: 10.1109/LGRS.2019.2943141
• ISSN: 1545-598X

In the intricate infrared cloudy-sky background, the edge of cloud might be falsely detected as a target because it is usually similar to a small target in contrast and complexity, which are the criteria that conventional small target detection methods adopt to differentiate between background and target. However, the shape of a small target is isotropic and similar to 2-D Gaussian function, while the strong background edge is anisotropic and tends to spread in a certain direction. In this letter, we propose a method to detect small targets in the intricate infrared cloudy-sky background by a local difference adaptive measure (LDAM). This proposed method uses the local structure tensor to perceive the dominant direction and its uncertainty in the local infrared image and then sets the direction and shape of the filter to calculate the local difference. In this way, the background edge is estimated accurately and suppressed effectively. Extensive experiments show that the proposed method outperforms the baseline methods.