Paper
18 October 2002 Mean Squared and Worst Case performance of multiple spacecraft interferometric imaging systems : A Feature Based Approach
Author Affiliations +
Proceedings Volume 4902, Optomechatronic Systems III; (2002) https://doi.org/10.1117/12.467721
Event: Optomechatronic Systems III, 2002, Stuttgart, Germany
Abstract
The problem of quantifying minimum acceptable performance of multi-spacecraft interferometric imaging systems is considered. The noise corrupting the measurements is critical in the design of these systems and is dependent on the motion of the constituent spacecrafts. Minimum acceptable performance is defined in terms of the misclassification error of an image given that the set of images has been partitioned into two distinct classes. Two measures of the noise corrupting the measurements are considered: mean squared error(MSE) and the worst case error(WCE). It is shown that these are consistent with the goal of image classification in the sense that as image estimates converge in the MSE/WCE sense, the probability of misclassifying the image tends to zero. Error bounds are obtained on the MSE/WCE such that some minimum acceptable performance, in terms of the probability of correctly classifying an image, is acheived. An example is presented where the bandedness of the image of a planet is sought to be detected. Bounds on the noise corrupting the measurements are obtained such that a pre-specified level of performance is achieved for this case.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Suman Chakravorty, Pierre T. Kabamba, and David C. Hyland "Mean Squared and Worst Case performance of multiple spacecraft interferometric imaging systems : A Feature Based Approach", Proc. SPIE 4902, Optomechatronic Systems III, (18 October 2002); https://doi.org/10.1117/12.467721
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image classification

Signal detection

Planets

Imaging systems

Signal to noise ratio

Image analysis

Lithium

Back to Top