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The 3.7-|jim channel on-board the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) provides the unique capability to detect small, but hot, surface features. We present an image-processing technique based on a pixel-by-pixel subtraction of 10.8 |xm from 3.7 inn brightness temperatures. We also develop an automated technique which classifies hotspots based on: 1) the brightness temperatures at 3.7 and 10.8 |jim at a given pixel, and 2) a background temperature based on the immediately surrounding pixels.

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The existence of a low-level

The U.S. Navy has plans to develop an automated system to analyze satellite imagery aboard its ships at sea. Lack of time for training, in combination with frequent personnel rotations, precludes the building of extensive imagery interpretation expertise by shipboard personnel. A preliminary design starts from pixel data from which clouds are classified. An image segmentation is performed to assemble and isolate cloud groups, which are then identified (e.g., as a cold front) using neural networks. A combination of neural networks and expert systems is subsequently used to transform key information about the identified cloud patterns as inputs to an expert system that provides sensible weather information, the ultimate objective of the imagery analysis.