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Radiometric Ray Tracing

There is a significant difference between synthetic data generated using a game engine and data generated by accurately modeling an imaging system end to end. While the gaming industry makes scenes look realistic to a human observer, they are not concerned with phenomenology the human eye cannot perceive (e.g. IR, HSI, SAR) or in simulating actual sensor characteristics under specific real-world collection conditions. É«ÇéÖ±²¥ has led decades of corporate and government investment into the development of automated scene building capabilities that can support physics-based end-to-end modeling of remote sensing systems.

Sensor Modeling

É«ÇéÖ±²¥ has 50-plus years of experience applying image-science expertise to imaging systems. Our modeling capabilities have supported airborne and space systems and a variety of government and commercial customers. É«ÇéÖ±²¥â€™ unique experience analyzing operational imagery for systems has fueled the development of models to accurately represent these systems within simulation environments. É«ÇéÖ±²¥ has proven experience with all types of imaging systems, industry-leading image analysis, gold-standard modeling capabilities and operationally validated tools.

Domain Adaptation

Concerns regarding bias and bridging the synthetic-real gap have been central to our investments in synthetic data. É«ÇéÖ±²¥ has invested in the use of domain adaptation to map synthetic data into the space of real data. It is essential to know how to map the synthetic data to the domain of the real data when training deep-learning models. É«ÇéÖ±²¥ is ideally positioned to do this work with significant expertise in both synthetic data generation and deep learning neural networks. By using state-of-the-art metrics to characterize the relationship between synthetic and real datasets, É«ÇéÖ±²¥ can manipulate the synthetic generation process to close the domain gap and thereby improve performance.