Detecting low-emissivity objects in LWIR hyperspectral data and the corresponding impact on atmospheric compensation

Robert Daniel Kaiser; David Lee Vititoe; Aaron Keith Andrews

doi:10.1117/12.497122

23 September 2003 Detecting low-emissivity objects in LWIR hyperspectral data and the corresponding impact on atmospheric compensation

Robert Daniel Kaiser, David Lee Vititoe, Aaron Keith Andrews

Author Affiliations +

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.497122
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Several of the leading atmospheric compensation algorithms for LWIR hyperspectral data require the detection and exclusion low-emissivity objects from the analysis. In this paper, nine different methods for detection of low-emissivity objects are presented. In testing, it was found that the algorithms proposed suffered from temperature sensitivities. Further testing was accomplished without filtering to test the performance of Scaled and Unscaled ISAC under a range of environmental and system parameters. Detection performance is quantified directly in terms of probability of detection vs. probability of false alarm and in terms of atmospheric state parameters.

Citation Download Citation

Robert Daniel Kaiser, David Lee Vititoe, and Aaron Keith Andrews "Detecting low-emissivity objects in LWIR hyperspectral data and the corresponding impact on atmospheric compensation", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.497122

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