Extensions and Modi cations of the Kohonen-SOM and Applications in Remote Sensing Image Analysis Thomas Villmann and Erzsébet Merényi University Leipzig, Clinic of Psychotherapy Karl-Tauchnitz-Str. 25 04107 Leipzig, Germany Email: villmann@informatik.uni-leipzig.de Rice University Depatment of Electrical and Computer Engineering 6100 Main Street, MS 380 Houston, Texas, U.S.A. Email: erzsebet@ece.rice.edu Summary. Utilization of remote sensing multi- and hyperspectral imagery has shown a rapid increase in many areas of economic and scienti c signi cance over the past ten years. Hy- perspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, however, has proved an insurmountable challenge for many tra- ditional classi cation, clustering and visualization methods. This chapter presents spectral image analyses with Self-Organizing Maps (SOMs). Several recent extensions to the original Kohonen SOM are discussed, emphasizing the necessity of faithful topological mapping for correct interpretation. The effectiveness of the presented approaches is demonstrated through case studies on real-life multi- and hyperspectral images. 1 Introduction Airborne and satellite-borne remote sensing spectral imaging has become one of the most advanced tools for collecting vital information about the surface of Earth and other planets. The utilization of these data includes areas such as mineral ex- ploration, land use, forestry, ecosystem management assessment of natural hazards, water resources, environmental contamination, biomass and productivity and many other activities of economic signi cance, as well as prime scienti c pursuits such as looking for possible sources of past or present life on other planets. The number of applications has dramatically increased in the past ten years with the advent of imaging spectrometers that greatly surpass traditional multi-spectral sensors (e.g., Landsat Thematic Mapper (TM)). Imaging spectrometers can resolve the known, unique, discriminating spectral features of minerals, soils, rocks, and vegetation. While a multi-spectral sensor samples a given wavelength window (typically the in: U.Seiffert and L.C. Jain (Eds.), Self-Organizing Maps. Recent Advances and Applications. Springer-Verlag Berlin, p. 121–145, 2001.