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Proceedings Paper

A comparison of techniques to model and measure spatial resolution in hyperspectral imaging sensors
Author(s): A. D. Cropper; David C. Mann
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Paper Abstract

When characterizing the imaging performance of tactical airborne imagers, one must account for the inherent subjectivity of human operators interpreting objects within a scene. One common approach to measuring imaging performance is the National Imagery Interpretability Rating Scale (NIIRS). However, the ground resolvable distance (GRD) is often a preferred metric because it can more easily be predicted through analysis, measured with field data, and traced to the laboratory measurements of system components. Although GRD is a calculable metric, it ultimately contains some ambiguity when linking it to an operator’s experience. Because of this, the ISR community sometimes utilizes different approaches to modeling and even measuring GRD. This can lead to confusion and disagreements when trying to compare the performance of different sensors. This paper explores different methods for modeling and measuring GRD. Emphasis is placed on airborne hyperspectral imaging (HSI) sensors. The additional spectral information provided by HSI sensors allows one to explore the relative importance of spatial resolution within the framework of HSI target detection capabilities. Modeled HSI performance will be validated against real world measurements performed with different airborne hyperspectral sensors. Different modeling and measurement approaches will be evaluated based on flight imagery, and the relative strengths and weaknesses of these different approaches will be discussed. In particular, a new model is presented, which is based on methods from laser physics of measuring the width of the point spread function.

Paper Details

Date Published: 7 October 2019
PDF: 13 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115526 (7 October 2019); doi: 10.1117/12.2532405
Show Author Affiliations
A. D. Cropper, Raytheon Co. (United States)
David C. Mann, Raytheon Co. (United States)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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