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15 October 2013 Experiments and models of active and thermal imaging under bad weather conditions
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Abstract
Thermal imaging cameras are widely used in military contexts for their night vision capabilities and their observation range; there are based on passive infrared sensors (e.g. MWIR or LWIR range). Under bad weather conditions or when the target is partially hidden (e.g. foliage, military camouflage) they are more and more complemented by active imaging systems, a key technology to perform target identification at long range. The 2D flash imaging technique is based on a high powered pulsed laser source that illuminates the entire scene and a fast gated camera as the imaging system. Both technologies are well experienced under clear meteorological conditions; models including atmospheric effects such as turbulence are able to predict accurately their performances. However, under bad weather conditions such as rain, haze or snow, these models are not relevant. This paper introduces new models to predict performances under bad weather conditions for both active and infrared imaging systems. We point out their effects on controlled physical parameters (extinction, transmission, spatial resolution, thermal background, speckle, turbulence). Then we develop physical models to describe their intrinsic characteristics and their impact on the imaging system performances. Finally, we approximate these models to have a “first order” model easy to deploy for industrial applications. This theoretical work will be validated on real active and infrared data.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erwan Bernard, Nicolas Riviere, Mathieu Renaudat, Pierrick Guiset, Michel Pealat, and Emmanuel Zenou "Experiments and models of active and thermal imaging under bad weather conditions", Proc. SPIE 8897, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing, 889703 (15 October 2013); https://doi.org/10.1117/12.2028978
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