The launching of the first LANDSAT satellite in 1972 suddenly provided us with a vast amount of relatively cheap remotely sensed data. It was hoped that this freely available data would initiate a dramatic improvement in our ability to map and to monitor the earth's surface. A considerable amount of effort has been expended in demonstrating the potential effectiveness of LANDSAT in solving some of the more pressing requirements of the various resource disciplines. In spite of the apparent success of some of these demonstrations, the operational use of LANDSAT data has not lived up to expectations.
Many factors have contributed to this disappointing performance in particular the long time delay between data acquisition and dissemination, the non-availability of geometrically corrected data and the high cost of effective data analysis and interpretation.
Continuing investment into pre-processing equipment on the part of the responsible government agencies will largely alleviate problems associated with data quality and availability, but unfortunately the cost of image analysis will continue to be born directly by the end user.
The launching of LANDSAT D, SPOT and other remote sensing satellites will further exacerbate this situation, with an increase in both the quality and quantity of remotely sensed data. Consequently, an effective solution to this problem must be found within the near future.
This paper describes an approach which would result in a significant overall reduction in the cost of image analysis by spreading the capital outlay needed for a practical image analysis system over a number of end users.
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