Fourier Feature Fusion (FFF): A Multi-int, Multi-Look 3D Feature Fusion Algorithm

The proposed approach provides a means for developing novel, computationally efficient multi-int, multi-look fusion algorithms.  The proposed approach explores a combination of feature detection, extraction, and matching methods with novelty in uniquely applying discrete Fourier transforms to feature data to provide real-time matching and fusing of multi-int, multi-look data.  Image registration techniques use discrete Fourier transforms to match features at the subpixel level and have been potential to provide real-time 3D feature fusion capabilities that can be directly fed into Machine Learning models for interpretation. D3 Autonomy calls this approach: Fourier Feature Fusion (FFF).  D3 Autonomy will explore advanced solutions in feature extraction and object detection/identification methods, aiming to minimize human oversight and computational complexity in fusion processes. Our focus will be on developing a conceptual design for Phase II detection/classification algorithms, with a comprehensive Phase I report detailing the proposed algorithms, including demonstrations of electro-optic, infrared, and radar data processing, and low-level feature fusion from image data. Emphasis will be placed on computational efficiency to enable real-time capabilities. The report will clearly specify any data requirements and underlying assumptions, and the source code for the final demo will be provided as a key deliverable.