Classification of Spectrally-Similar Land Cover Using Multi-Spectral Neural Image Fusion and the Fuzzy ARTMAP Neural Classifier

Multi-spectral imagery from earth observation satellites has been widely used for land cover classification over the past two decades; however these classifications have generally been limited to broad categories. The ability to accurately identify sub-categories of land cover within these broad categories using widely available remotely sensed imagery is highly desirable for many applications. This paper assesses the benefits of new biologically-based image fusion and fused data mining methods for improving discrimination between spectrally-similar land cover classes using remotely sensed multi- spectral imagery. For this investigation multi-season Landsat imagery of a forest region in central New York State was processed using opponent-color image fusion, multi-scale visual texture and contour enhancement, and the fuzzy ARTMAP neural classifier. This approach is shown to enable identification of individual species of coniferous forest and improve classification accuracy compared to traditional statistical methods.