Application of decision tree technology for image classification using remote sensing data

Hyperspectral images of plots, cropped with silage- or grain corn and cultivated with conventional tillage, reduced tillage, or no till, were classified using the classification and regression tree (C and RT) approach, an innovative intelligent computational algorithm in data mining. Each tillage/cropping combination was replicated three times; for a total of 18 plots. Five hyperspectral reflectance measurements per plot were taken randomly to obtain a total of 90 measurements. Images were taken on June 30, August 5 and August 25, 2000 to reflect three stages of crop development. Each measurement consisted of reflectances in 71 wavelengths ranging from 400 to 950 nm. C and RT models were developed separately for the three observation dates, using the 71 reflectances as inputs to classify the image according to: (a) practice; (b) residue level, (c) cropping practices, (d) tillage/cropping (residue) combination. C and RT models could generally distinguish tillage practices with a classification accuracy of 0.89 and residue levels with a classification accuracy of 0.98.