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Exploring EO vehicle recognition performance using manifolds as a function of lighting condition variability

Novel techniques are necessary in order to improve the current state-of-the-art for Aided Target Recognition (AiTR) especially for persistent intelligence, surveillance, and reconnaissance (ISR). A fundamental assumption that current AiTR systems make is that operating conditions remain semi-consistent between the training samples and the testing samples. Today’s electro-optical AiTR systems are still not robust to common occurrences such as changes in lighting conditions. In this work, we explore the effect of systemic variation in lighting conditions on vehicle recognition performance. In addition, we explore the use of low-dimensional nonlinear representations of high-dimensional data derived from electro-optical synthetic vehicle images using Manifold Learning - specifically Diffusion Maps on recognition. Diffusion maps have been shown to be a valuable tool for extraction of the inherent underlying structure in high-dimensional data.