Infrared Small Target Detection Based on Multidirectional Cumulative Measure
Robustness of small target detection is a researchable hotspot in infrared (IR) surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. The algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations d...
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Published in | IEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
2023
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Subjects | |
Online Access | Get full text |
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Summary: | Robustness of small target detection is a researchable hotspot in infrared (IR) surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. The algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations due to the long time consumption. This letter proposes a multi-directional cumulative measure (MDCM) to enhance the saliency and effectiveness of weak-small target detection. First, multi-directional cumulative mean difference is implemented in central layer and background layer to estimate the background, while multi-directional cumulative derivative multiplying (MDCDM) is calculated in central-active layer to characterize the overall target’s heterogeneity, and then the technology of image fusion is adopted to eliminate the interference of false target. Finally, a simple adjudicative technology is employed toward separated target region from complex scenes. Compared to up-to-date existing approaches, extensive simulational testing on four public datasets prove that the proposed approach is capable of separating small targets efficiently from an irregular background in a single-scale window and achieving a comparable or even better accuracy. |
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ISSN: | 1545-598X 1558-0571 |
DOI: | 10.1109/LGRS.2023.3284662 |