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Proceedings Paper

Dynamic hyperspectral imaging
Author(s): Marina V. A. Murzina; J. Paul Farrell
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Paper Abstract

Bad things often happen fast. This means that we need to react fast. In this work, we develop the technology that allows one to identify and characterize fast events. In real time, we dynamically process hyperspectral information of a scene, specifically analyzing its temporal behavior. The goal is to detect fast and super-fast events like explosions, fast-moving objects and instant changes in the chemical composition of air and other materials. Until recently, the enormous quantity of hyperspectral information confined us to static hyperspectral data processing. Hyperspectral techniques were used for finding certain objects, chemicals, or anomalies in a picture, frame by frame, statically. Dynamic (temporal) analysis was developed primarily for astrophysical applications performed a long time after the frames had been captured. In this work, we study ways of taking advantage of emerging hardware technologies that allow one to look at hyperspectral information dynamically: by characterizing temporal changes as they occur. We apply methods from astrophysics (supernova observations) and present our unique algorithms for contemporaneous dynamical analysis of hyperspectral data. The application addresses the question: have there been any sudden changes in the hyperspectral pattern of a scene? If there were sudden changes, were those changes related to a substantial energy release? These questions do not depend on assumptions about specific spectral patterns, chemical composition, or shapes: we look for any changes in a scene. Such dynamical analysis can therefore allow one to react promptly to fast events without prior knowledge about what occurred. This paper addresses issues specific to dynamic (as opposed to static) hyperspectral imaging, algorithmic approaches to dynamic hyperspectral data processing, and associated hardware-implementation issues.

Paper Details

Date Published: 9 May 2005
PDF: 10 pages
Proc. SPIE 5769, Nondestructive Detection and Measurement for Homeland Security III, (9 May 2005); doi: 10.1117/12.598620
Show Author Affiliations
Marina V. A. Murzina, Brookhaven Technology Group (United States)
J. Paul Farrell, Brookhaven Technology Group (United States)


Published in SPIE Proceedings Vol. 5769:
Nondestructive Detection and Measurement for Homeland Security III
Aaron A. Diaz; A. Emin Aktan; H. Felix Wu; Steven R. Doctor; Yoseph Bar-Cohen, Editor(s)

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