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Proceedings Paper

Multiresolution fusion of FLIR and ladar data for target detection with JDEF
Author(s): Stelios C.A. Thomopoulos; Byron H. Chen
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Paper Abstract

A joint detection and estimation filter (JDEF) is proposed for fusing Forward Looking Infrared (FLIR) and Laser Radar (Ladar) data for target detection. The JDEF has two main components: minimum mean square error estimator followed by a Bayesian detector. The estimator is a 2-D adaptive Kalman filter based on conditional Markovian field (CMF) modeling. The detector is a recursive maximum a posteriori probability (MAP) detector based on the generalized likelihood ratio. The estimator removes the noise and provides the detector with the estimated mean and variance at each pixel. The detector combines the information from the estimator as well as the information from the neighboring pixels to decide whether the current pixel belongs to targets or clutter. By fusing the data from FLIR and Ladar images with the JDEF, the locations of the possible targets are efficiently determined and the targets are accurately segmented. The filter has successfully been applied to both synthetic data and real data. The results are presented.

Paper Details

Date Published: 20 August 1993
PDF: 10 pages
Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); doi: 10.1117/12.150266
Show Author Affiliations
Stelios C.A. Thomopoulos, The Pennsylvania State Univ. (United States)
Byron H. Chen, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 2059:
Sensor Fusion VI
Paul S. Schenker, Editor(s)

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