Feature selection and land cover classification of a MODIS-like data set for a semiarid environment

The advent of the Earth Observing System (EOS), and the Moderateresolution Imaging Spectroradiometer (MODIS) in particular, will usher in a new era of global remote sensing by providing very large data volumes for interpretation and processing. Since many data streams will contain correlated data, feature selection is an important practical problem for such activities as classification of global land cover based on spectral, temporal, spatial and directional data. Treebased classification methods offer a suite of promising approaches to extraction of meaningful features from large measurement spaces. This research develops a tree-based model that performs feature selection on a satellite database containing information on land covers in a semiarid region in Cochise County, Arizona. In addition, we test the abilities of several classifiers to correctly label land cover using this reduced set of inputs under various sampling schemes. Results from this analysis indicate that decision trees can reduce a high-dimension dataset to a manageable set of inputs that retain most of the information of the original database, while remaining largely insensitive to choice of sampling strategy, and that Fuzzy ARTMAP, a type of artificial neural network classifier, achieves highest accuracy in comparison to maximum-likelihood or decision-tree classifiers.