A Kernel-Based Nonparametric Regression Method for Clutter Removal in Infrared Small-Target Detection Applications

Small-target detection in infrared imagery with a complex background is always an important task in remote-sensing fields. Complex clutter background usually results in serious false alarm in target detection for low contrast of infrared imagery. In this letter, a kernel-based nonparametric regressi...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2010-07, Vol.7 (3), p.469-473
Main Authors: Gu, Yanfeng, Wang, Chen, Liu, BaoXue, Zhang, Ye
Format: Article
Language:English
Subjects:
Clutter
Clutter removal
Concrete
constant false alarm rate (CFAR)
Filtering
Fluctuations
Infrared
Infrared detectors
infrared image
Infrared imagery
Infrared imaging
Kernel
kernel regression
Kernels
Mathematical models
Object detection
Pixel
Regression
Shape
Target detection
Tasks
Thyristors
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Summary:Small-target detection in infrared imagery with a complex background is always an important task in remote-sensing fields. Complex clutter background usually results in serious false alarm in target detection for low contrast of infrared imagery. In this letter, a kernel-based nonparametric regression method is proposed for background prediction and clutter removal, furthermore applied in target detection. First, a linear mixture model is used to represent each pixel of the observed infrared imagery. Second, adaptive detection is performed on local regions in the infrared image by means of kernel-based nonparametric regression and two-parameter constant false alarm rate (CFAR) detector. Kernel regression, which is one of the nonparametric regression approaches, is adopted to estimate complex clutter background. Then, CFAR detection is performed on "pure" target-like region after estimation and removal of clutter background. Experimental results prove that the proposed algorithm is effective and adaptable to small-target detection under a complex background.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2009.2039192