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A new approach for feature tracking and recognition on sequential satellite sensor images using neural networks has been developed. Feature tracking is recognized as being of importance in applications such as ice-mapping, cloud motion winds, ocean currents, and short-term forecasting. Feature recognition ® nds application in automatic image navigation. This paper explores the potential of a Hop® eld neural network to perform feature tracking or recognition, and gives examples of its implementation to three di erent applications. It is shown that the net can provide superior performance to existing techniques for tracking, the advantages of this new approach being its precision, speed, low sensitivity to deformation, its capacity to detect rotational motion, and to provide directly the cross-and along-isopycnal components of displacement vectors. It is also shown that the Hop® eld neural network can provide a valuable tool for automatic image navigation through coastline recognition.

Abstract-As an intermediate solution betweenGlaser's satellite solar power (SSP) and ground-based photovoltaic (PV) panels, this paper examines the collection of solar energy using a highaltitude aerostatic platform. A procedure to calculate the irradiance in the medium/high troposphere, based on experimental data, is described. The results show that here a PV system could collect about four to six times the energy collected by a typical U.K.-based ground installation, and between one-third and half of the total energy the same system would collect if supported by a geostationary satellite (SSP). The concept of the aerostat for solar power generation is then briefly described together with the equations that link its main engineering parameters/variables. A preliminary sizing of a facility stationed at 6 km altitude and its costing, based on realistic values of the input engineering parameters, is then presented.

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