Multispectral simulation environment for modeling low-light-level sensor systems

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Title: Multispectral simulation environment for modeling low-light-level sensor systems
Author: Ientilucci, Emmett; Brown, Scott; Schott, John; Raqueno, Rolando
Abstract: Image intensifying cameras have been found to be extremely useful in low-light-level (LLL) scenarios including military night vision and civilian rescue operations. These sensors utilize the available visible region photons and an amplification process to produce high contrast imagery. It has been demonstrated that processing techniques can further enhance the quality ofthis imagery. For example, fusion with matching thermal JR imagery can improve image content when very little visible region contrast is available. To aid in the improvement of current algorithms and the development of new ones, a high fidelity simulation environment capable ofproducing radiometrically correct multi-band imagery for low-light-level conditions is desired. This paper describes a modeling environment attempting to meet these criteria by addressing the task as two individual components: (i) prediction ofa low-light-level radiance field from an arbitrary scene, and (ii) simulation of the output from a low-light-level sensor for a given radiance field. The radiance prediction engine utilized in this environment is the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model which is a first principles based multi-spectral synthetic image generation model capable of producing an arbitrary number of bands in the 0.28 to 20 pm region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then processed by a user configurablemulti-stage low-light-level sensor model that applies the appropriate noise and modulation transfer function (MTF) at each stage in the image processing chain. This includes the ability to reproduce common intensifying sensor artifacts such as saturation and "blooming". Additionally, co-registered imagery in other spectral bands may be simultaneously generated for testing fusion and exploitation algorithms. This paper discusses specific aspects ofthe DIRSIG radiance prediction for low-light-level conditions including the incorporation ofnatural and man-made sources which emphasizes the importance ofaccurate BRDF. A description of the implementation of each stage in the image processing and capture chain for the LLL model is also presented. Finally, simulated images are presented and qualitatively compared to lab acquired imagery from a commercial system.
Description: RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/
Record URI: http://hdl.handle.net/1850/4229
Publishers URL: http://dx.doi.org/10.1117/12.331229
Date: 1998-11

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