FPGA-based hyperspectral covariance coprocessor for size, weight, and power constrained platforms

Size, weight, and power (SWaP) are important factors in the design of any remote sensing platform. These remote sensing platforms, such as Unmanned Air Vehicles (UAVs) and microsatellites, are becoming increasingly small. This creates a need for remote sensing and image processing hardware that consumes less area, weight, and power, while delivering processing performance. It is also advantageous to utilize the same hardware for multiple platform tasks. The purpose of this research is to design and characterize an FPGA-based hardware coprocessor that parallelizes the calculation of covariance, a time-consuming step common in hyperspectral image processing. Our design is compared to a CPU-based implementation and shown to have an overall SWaP advantage. We evaluate our coprocessor using a metric that is useful in the consideration of future SWaP-constrained remote sensing platforms: floating point operations per Watt-kg (FLOPs/W-kg). Additional hardware capacity exists in our design to implement other remote sensing platform tasks.