Open Source Layered Sensing Model

This paper will look at using open source tools (Blender (c) [17], LuxRender (c) [18], and Python (c) [19]) to build an image processing model for exploring combinations of sensors/platforms for any given image resolution. The model produces camera position, camera attitude, and synthetic camera data that can be used for exploitation purposes. We focus on electro-optical (EO) visible sensors to simplify the rendering but this work could be extended to use other rendering tools that support different modalities. Due to the computational complexity of ray tracing we employ the Amazon Elastic Cloud Computer to help speed up the generation of large ray traced scenes. The key idea of the paper is to provide an architecture for layered sensing simulation which is modular in design and constructed on open-source off-the-shelf software. This architecture shows how leveraging existing open-source software allows for practical layered sensing modeling to be rapidly assimilated and utilized in real-world applications. In this paper we demonstrate our model output is automatically exploitable by using generated data with an innovative video frame mosaic algorithm.