Memory efficient atmospheric effects modeling for infrared scene generators

Çağlar Kavak; Seçkin Özsaraç

doi:10.1117/12.2176852

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12 May 2015 Memory efficient atmospheric effects modeling for infrared scene generators

Çağlar Kavak, Seçkin Özsaraç

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Proceedings Volume 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI; 945212 (2015) https://doi.org/10.1117/12.2176852
Event: SPIE Defense + Security, 2015, Baltimore, Maryland, United States

Abstract

The infrared (IR) energy radiated from any source passes through the atmosphere before reaching the sensor. As a result, the total signature captured by the IR sensor is significantly modified by the atmospheric effects. The dominant physical quantities that constitute the mentioned atmospheric effects are the atmospheric transmittance and the atmospheric path radiance. The incoming IR radiation is attenuated by the transmittance and path radiance is added on top of the attenuated radiation. In IR scene simulations OpenGL is widely used for rendering purposes. In the literature there are studies, which model the atmospheric effects in an IR band using OpenGLs exponential fog model as suggested by Beers law. In the standard pipeline of OpenGL, the related fog model needs single equivalent OpenGL variables for the transmittance and path radiance, which actually depend on both the distance between the source and the sensor and also on the wavelength of interest. However, in the conditions where the range dependency cannot be modeled as an exponential function, it is not accurate to replace the atmospheric quantities with a single parameter. The introduction of OpenGL Shading Language (GLSL) has enabled the developers to use the GPU more flexible. In this paper, a novel method is proposed for the atmospheric effects modeling using the least squares estimation with polynomial fitting by programmable OpenGL shader programs built with GLSL. In this context, a radiative transfer model code is used to obtain the transmittance and path radiance data. Then, polynomial fits are computed for the range dependency of these variables. Hence, the atmospheric effects model data that will be uploaded in the GPU memory is significantly reduced. Moreover, the error because of fitting is negligible as long as narrow IR bands are used.

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Çağlar Kavak and Seçkin Özsaraç "Memory efficient atmospheric effects modeling for infrared scene generators", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 945212 (12 May 2015); https://doi.org/10.1117/12.2176852

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