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首页> 外文会议>Conference on image intensifiers and applications, and characteristics and consequences of space debris and near-earth objects >Multispectral simulation environment for modeling low-light-level sensor systems
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Multispectral simulation environment for modeling low-light-level sensor systems

机译：用于造型低光级传感器系统的多光谱仿真环境

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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 of this imagery. For example, fusion with matching thermal IR 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 of producing 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: (1) prediction of a low-light-level radiance field from an arbitrary scene, and (2) 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 micrometer region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then processed by a user configurable multi-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 of the DIRSIG radiance prediction for low- light-level conditions including the incorporation of natural and man-made sources which emphasizes the importance of accurate 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.
机译：已发现图像强化相机在低光级（LLL）场景中非常有用，包括军事夜视和民用救援行动。这些传感器利用可用的可见区域光子和放大过程来产生高对比度图像。已经证明了处理技术可以进一步提高此图像的质量。例如，当具有匹配的热IR图像的融合可以在非常小的可见区域可用时改善图像内容。为了帮助改进当前算法和新的算法，需要一种高保真模拟环境，其能够为低光级条件产生辐射校正的多频带图像。本文介绍了尝试通过以两个单独的组件解决任务来满足这些标准的建模环境：（1）从任意场景的低光级Radiance字段预测（2）从低电平的输出仿真用于给定Radiance场的光电电平传感器。在这样的环境中使用的辐射预测引擎是数字成像和遥感图像生成（DIRSIG）模型，该模型是基于第一原理的多光谱合成图像生成能够在0.28至20微米的区域产生的频带的任意数量的模型。 DIRSIG模型用于产生高空间和光谱分辨率辐射场图像。然后通过用户可配置的多级低电平传感器模型处理这些图像，该低级低电平传感器模型在图像处理链中的每个阶段应用适当的噪声和调制传递函数（MTF）。这包括以再现共同加强传感器工件，如饱和度和能力“开花”。另外，可以同时生成用于测试融合和开发算法的其他光谱带中的共登记图像。本文讨论了针对低光级条件的DIRSIG辐射预测的具体方面，包括掺入自然和人造来源，强调了精确的BRDF的重要性。在用于LLL模型图像处理和捕获链每个阶段的执行情况的描述也呈现。最后，与来自商业系统的实验室获取的图像相比，呈现和定性地呈现模拟图像。

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