VNIR hyperspectral background characterization methods in adverse weather conditions

João M. Romano; Dalton Rosario; Luz Roth

doi:10.1117/12.818561

27 April 2009 VNIR hyperspectral background characterization methods in adverse weather conditions

João M. Romano, Dalton Rosario, Luz Roth

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 733409 (2009) https://doi.org/10.1117/12.818561
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Hyperspectral technology is currently being used by the military to detect regions of interest where potential targets may be located. Weather variability, however, may affect the ability for an algorithm to discriminate possible targets from background clutter. Nonetheless, different background characterization approaches may facilitate the ability for an algorithm to discriminate potential targets over a variety of weather conditions. In a previous paper, we introduced a new autonomous target size invariant background characterization process, the Autonomous Background Characterization (ABC) or also known as the Parallel Random Sampling (PRS) method, features a random sampling stage, a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results at the end. In this paper, we will demonstrate how different background characterization approaches are able to improve performance of algorithms over a variety of challenging weather conditions. By using the Mahalanobis distance as the standard algorithm for this study, we compare the performance of different characterization methods such as: the global information, 2 stage global information, and our proposed method, ABC, using data that was collected under a variety of adverse weather conditions. For this study, we used ARDEC's Hyperspectral VNIR Adverse Weather data collection comprised of heavy, light, and transitional fog, light and heavy rain, and low light conditions.

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João M. Romano, Dalton Rosario, and Luz Roth "VNIR hyperspectral background characterization methods in adverse weather conditions", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 733409 (27 April 2009); https://doi.org/10.1117/12.818561

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KEYWORDS

Target detection

Detection and tracking algorithms

Parallel computing

Mahalanobis distance

Sensors

Fiber optic gyroscopes

Hyperspectral imaging

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