A prototype operational data-processing system has been defined, implemented, and tested by Multispectral Analysis Section personnel of the Environmental Research Institute of Michigan (ERIM). This system has been designed for the classification and mensuration of agricultural crops through the use of data provided by the LANDSAT satellite scanner. The specific crops for which the system was designed are the small grains including wheat, rye, oats, and barley, although the system as designed is not limited to these particular crops.

The processing system, known as PROCAMS (Prototype Classification and Mensuration System), has been built based on the experience gained and to overcome the difficulties with previous processing systems. PROCAMS takes advantage of advanced techniques and understanding to help reduce the need for classifier training information, however, in its present form it still depends on human intervention and assistance for training. The PROCAMS is designed to take advantage of multitemporal coverage while simultaneously recognizing the reality of the limited availability of complete multitemporal coverage and providing a means for accommodating multiple training sites. (This system also handles the more conventional unitemporal single training site situation.) Also addressed by PROCAMS are the dataprocessing problems associated with partial cloud cover, bad data lines, as well as changing sun angle and atmospheric state.

The PROCAMS, as presently defined, provides for the use of many options depending on the characteristics of the available data. Incorporated as part of PROCAMS are advanced data transformation and signature extension algorithms to allow for the use of signatures over large areas and a variety of measurement conditions.

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