Wetland classification using optical and radar data and neural network classification

Abstract

A study was conducted to investigate the ability of a neural network based classification technique to delineate upland and forested wetland areas and to distinguish between different levels of wetness in a forested wetland. NASA's Airborne Terrestrial Applications Sensor (ATLAS) multi-spectral data and Airborne Imaging Radar Synthetic Aperture Radar (AIRSAR) data were used in this study. A National Wetland Inventory (NWI) map served as a reference. Cascade-correlation, a feed-forward neural network architecture, was employed as the classifier. The neural network technique separated upland from wetland spectral signatures and discriminated two out of four different water regimes identified by the NWI within the wetland area. The relative usefulness of ATLAS and AIRSAR data for wetness classification was also investigated. It was found that both data sources, when used in isolation, could separate wetland from upland about equally well, but better performance was observed when these data sources were combined.