Bayesian approach to image recovery of closely spaced objects

Nielson Wade Schulenburg; John A. Hackwell

doi:10.1117/12.157812

22 October 1993 Bayesian approach to image recovery of closely spaced objects

Nielson Wade Schulenburg, John A. Hackwell

Proceedings Volume 1954, Signal and Data Processing of Small Targets 1993; (1993) https://doi.org/10.1117/12.157812
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

A technique is described for recovering positional and radiometric information on unresolved objects that are so closely spaced that their individual blur functions overlap. Emphasis is on point sources. A Bayesian Spectral Analysis method has been modified to two dimensions and applied to resolving 'clumps' of objects for both simulated and real data. The method is able to judge the amount of noise in the data and provide error bars in the individual pulse positions and amplitudes from a single data set rather than from the deviations observed after measuring many independent sets of data. The Bayesian technique can also estimate the number of discrete objects in a given clump. Noisy simulated data containing three sources was fitted by a one-, two-, three-, and four- source model. By the way it formulates the model, the Bayesian approach naturally includes a factor which reflects the reduction in the number of degrees of freedom for a model with a greater number of sources. As a result, the algorithm gives a higher probability for the three-source model than for the four-source model while resoundingly rejecting the one- and two-source models. The estimated centroids and amplitudes are shown to agree with the truth within the derived error bars to the degree expected by gaussian errors. Studies of data taken during a flight test by a sensor that measured a scene simultaneously in the visible and long-wavelength regions show that positional information derived from visible-wavelength data can be 'fused' with infrared images to derive the LWIR intensities of individual objects in a unresolved clump. The estimated LWIR intensities using the visible assist are shown to be an improvement over working with the LWIR data alone.

Citation Download Citation

Nielson Wade Schulenburg and John A. Hackwell "Bayesian approach to image recovery of closely spaced objects", Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993); https://doi.org/10.1117/12.157812

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