Six advanced very high resolution radiometer local area coverage arctic scenes are classified into 10 classes. These include water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over ice, cumulus over water, and multilayer cloudiness. Eight spectral and textural features are computed. The textural features are based upon the gray level difference vector method. Six different artificial intelligence classifiers are examined: (1) the feed forward back propagation neural network; (2) the probabilistic neural network; (3) the hybrid back propagation neural network; (4) the "don• care" perceptton network; (5) the "don• care" back propagation neural network; and (6) a fuzzy logic-based expert system. Accuracies in excess of 95% are obtained for all but the hybrid neural network. The "don• care" back propagation neural network produces the highest accuracies and also has low CPU requirements. Thin fog/stratus over ice is the class consistently with the lowest accuracy, otten misclassified as broken sea ice. Water, land, cirrus over ice, and snow-covered mountains are all classified with high accuracy (>98%). The high accuracy achieved in the present study can be traced to (1) accurate classifiers; (2) an excellent choice for the feature vector; and (3) accurate labeling. A sophisticated new interactive visual image classification system is used for the labeling. 1. INTRODUCTION With the growing awareness and debate over the potential changes associated with global climate change, the polar regions are receiving increased attention. Since greenhouse forcings are expected to be amplified in polar regions [WetheraM and Manabe, 1986; Schlesinger and Mitchell, 1987; Steffen and Lewis, 1988], these regions may act as early warning indicators of actual climate shifts. Global cloud distributions can be expected to be altered with increased greenhouse forcings. In the polar regions, cloud cover changes can be expected to have a significant effect on sea ice conditions [Shine and Crane, 1984] and on regional ice-albedo feedback [Barry et al., 1984]. In particular, polar stratus is very important to the polar heat balance and directly affects surface melting [Parkinson et al., 1987]. However, in order to monitor changes in polar surface conditions and polar cloudiness, more accurate procedures must be developed to distinguish between cloud and snow-covered surfaces.Owing to the similarity of cloud and snow-ice spectral signatures in both visible and infrared wavelengths, it is difficult to distinguish clouds from surface features in the polar regions. In the visible channels, thin ice, ice fragments, wet ice, and pancake ice have low albedoes and can be misinterpreted as water, melt ponds, or as thin cloud/haze. Persistent surface inversions and low clouds in winter and near isothermal structure and extensive stratiform clouds in summer limit discrimination in the in•rrared
