Coral reef environments can be expected to be encountered relatively frequently in very shallow water mine countermeasures operations. A coral reef is a prime example of an environment where acoustical methods can be expected to have difficulty due to the high density of clutter. A prototype Fluorescence Imaging Laser Line Scan (FILLS) sensor has demonstrated that fluorescence imagery provides strong signatures that may be used to separate the coral clutter from mines. FELLS imagery demonstrates the ease with which a human observer can differentiate mine like objects from natural clutter in an environment that Is difficult for sonars. Accordingly, this technology is a leading candidate for extending MCM capabilities into highly cluttered environments. In this role, FELLS Imagery can be used for MLC detection, classification, and identification. Our prototype FELLS sensor utilizes a scanning blue laser and 4 independent, synchronously scanning receiver channels. One channel produces imagery from the elastically scattered (blue) reflected laser fight. The other three channels produce imagery from collected green, yellow, and red light. In coral reef environments, florescence processes produce this green, yellow, and red light. This fluorescence is concentrated in the corals, but may also occur in the surrounding sediment. In the clear water characteristic of coral reef environments, human observers may differentiate manmade objects from the coral clutter through shape analysis. While this process is "easy" for a trained human observer on an image-by-image basis, it is time consuming and tiring. It is highly desirable to develop automatic algorithms that win automatically highlight the manmade objects in the imagery, while rejecting the coral reef clutter in the imagery. The human observer will then be queued to focus his attention on a small set of objects for identification. This paper addresses the possibility of exploiting the fluorescence channels for the development of such an algorithm.
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