Real-time Multi-spectral Image Fusion

This paper describes a novel real-time multi-spectral imaging capability for surveillance applications. The capability combines a new high-performance multi-spectral camera system with a distributed algorithm that computes a spectral-screening Principal Component Transform (PCT). The camera system uses a novel filter wheel design together with a high-bandwidth CCD camera to allow image cubes to be delivered at 110 frames per second with spectral resolution between 400 and 1000 nm. The filters used in a particular application are selected to highlight a particular object based on its spectral signature. The distributed algorithm allows image streams from a dispersed collection of cameras to be disseminated, viewed, and interpreted by a distributed group of analysts in real-time. It operates on networks of commercial-off-the-shelf multiprocessors connected with high-performance (e.g. gigabit) networking, taking advantage of multi-threading where appropriate. The algorithm uses a concurrent formulation of the PCT to decorrelate and compress a multi-spectral image cube. Spectral screening is used to give features that occur infrequently (e.g. mechanized vehicles in a forest) equal importance to those that occur frequently (e.g. trees in the forest). A human-centered color-mapping scheme is used to maximize the impact of spectral contrast on the human visual system. To demonstrate the efficacy of the multi-spectral system, plant-life scenes with both real and artificial foliage are used. These scenes demonstrate the systems ability to distinguish elements of a scene, based on spectral contrast, that cannot be distinguished with the naked eye. The capability is evaluated in terms of visual performance, scalability, and real-time throughput. Our previous work on predictive analytical modeling is extended to answer practical design questions such as “For a specified cost, what system can should be constructed and what performance will it attain?