Abstract
Aircraft multispectral scanner (MSS) data collected by the NASA 24-channel MSS on July 26, 1972 (Mission 207) over the USDA, Research Farm at Weslaco, Texas, were evaluated for quality and for crop, soil, and water discrimination. The standard deviations for each of the 24 channels for a uniform surface, a water reservoir, were used as an indicator of system noise. By this criterion, channels 22, 20, 15, qnd 21 were of low quality. Based on the ratio of odd to even numbers in all channels, the conclusion was reached that the data are 7-bit precision. An optimum channel selection program selected channels 7, 8, 3, and 18 as the best 4 channels for distinguishing among seven vegetal categories: Stoneville 213 cotton, Anton SP-21 cotton, Valencia orange, Red Blush grapefruit, sugarcane, Coast-Cross 1 bermuda-grass and African stargrass. These same channels also distinguished the nonvegetal categories (water, highway, rooftops, and bare soil) satisfactorily. Among the vegetal categories, sugarcane and cotton had distinctive signatures that allowed them to be distinguished from grass and citrus. Classification accuracies improved to about 81% when the intra plant genera categories (such as the two cotton varieties) were combined into one.
Date of this Version
10-1973