Focal–plane processing architectures for real–time hyperspectral image processing

Real–time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal–plane optoelectronic–area IyO with a fine–grain, low-memory, single–instruction–multiple–data (SIMD) processor array is presented as an efficient computational solution for real–time hyperspectral image processing. The architecture is evaluated by use of realistic workloads to determine data throughputs, processing demands, and storage requirements. We show that traditional store–and–process system performance is inadequate for this application domain, whereas the focal–plane SIMD architecture is capable of supporting real–time performances with sustained operation throughputs of 500−1500 gigaoperationsys. The focal-plane architecture exploits the direct coupling between sensor and parallel–processor arrays to alleviate databandwidth requirements, allowing computation to be performed in a stream–parallel computation model, while data arrive from the sensors.