Application of PCA-CFAR-MNF to Image Enhancement for a UGV FLGPR

Abstract:

The UGV (Unmanned Ground Vehicle) FLGPR (Forward-Looking Ground Penetrating Radar) imaging model spatially variable characteristics lead to the relatively low image SCR (Signal-Clutter Ratio). In order to improve the image quality and improve the SCR, the coherence properties of the target in image sequences is used for image enhancement. That is, images from the same region at different time sequence are enhanced two times. First of all, CFAR (Constant False Alarm Rate) is a preliminary step to enhance the image sequence, and then PCA-CFAR-MNF enhancement algorithm is proposed: according to the iterative nature of the MNF (Maximum Noise Fraction), PCA (Principal Component Analysis) method is first used to transform selecting and then CFAR detection is used to estimate the image background in the iteration,. The measured data show that the proposed method not only keeping the target characteristics, but also have remarkable image enhancement effect, and have greatly improved SCR.

• Periodical: Applied Mechanics and Materials (Volumes 190-191)
• Pages: 656-660
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.190-191.656
• Online since: July 2012
• Authors: Han Hua Zhang, Yu Ming Wang, Qian Song
• Keywords: CFAR, Image Enhancement, MNF, Principal Component Analysis (PCA), UGV FLGPR

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