This paper presents a technique for automatic detection of the targets in forward-looking infrared (FLIR) imagery. Mathematical morphology is applied for the preliminary selection of possible regions of interest (ROI). An efficient clutter rejecter module based on probabilistic neural network is proposed, which is trained by using both target and background features to ensure excellent classification performance by moving the ROI in several directions with respect to the center of the detected target patch. Experimental results using real-life FLIR imagery confirm the excellent performance of the detector and the effectiveness of the proposed clutter rejecter module.
© 2009 Optical Society of America
Original Manuscript: May 30, 2008
Revised Manuscript: November 28, 2008
Manuscript Accepted: December 7, 2008
Published: January 12, 2009
Jesmin F. Khan, Mohammad S. Alam, and Sharif M. A. Bhuiyan, "Automatic target detection in forward-looking infrared imagery via probabilistic neural networks," Appl. Opt. 48, 464-476 (2009)