ART NEURAL NETWORKS FOR REMOTE-SENSING - VEGETATION CLASSIFICATION FROM LANDSAT TM AND TERRAIN DATA

A new methodology for automatic mapping from Landsat thematic mapper ( TM) and terrain data, based on the fuzzy ARTMAP neural network, is dev eloped. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for veget ation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, u sing sites not seen during training, Results are compared to those of maximum likelihood classifiers, as well as back propagation neural net works and K Nearest Neighbor algorithms, ARTMAP dynamics are fast, sta ble, and scalable, overcoming common limitations of back propagation. Best results are obtained using a hybrid system based on a convex comb ination of fuzzy ARTMAP and maximum likelihood predictions, A prototyp e remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automa tically constructs a minimal number of recognition categories to meet accuracy criteria, A voting strategy improves prediction and assigns c onfidence estimates by training the system several times on different orderings of an input set.