Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 7, Issue 3, Pages 469-473Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2009.2039192
Keywords
Clutter removal; constant false alarm rate (CFAR); infrared image; kernel regression; target detection
Categories
Funding
- National Natural Science Foundation of China [60972144, 60972143]
- Heilongjiang Province Postdoctoral Scientific and Research Foundation
- Research Fund for the Doctorial Program of Higher Education of China [20092302110033]
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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.
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