The ASTER polar cloud mask

This research is concerned with the problem of producing polar cloud masks for satellite imagery. The results presented are for Thematic Mapper (TM) data from the northern and southern polar regions, however, the techniques discussed will be applied to ASTER data when it becomes available. A series of classification techniques have been implemented and tested, the most promising of which is a neural network classifier. To use a neural network classifier, the pixels in the data must be transformed into feature vectors, some of which are used for training the network and the remainder of which are reserved for testing the final system. The first challenge is the identification of pure pixel samples from the imagery. The Interactive Visual Image Classification System (IVICS) was developed specifically for this project to make this task simpler for the human expert. After labeling the pixels, the feature vectors are generated. One hundred and forty potential vector elements, consisting of linear and nonlinear combinations of the satellite channel data, have been identified. Because smaller input vectors reduce the difficulty of training and can improve classification accuracy, the set of potential vector elements must be reduced. Two techniques have been tested: a histogram-based selection method and a fuzzy logic method. Both have proven effective for this task. Although the polar region is the only area considered in this work, a system that can produce cloud masks for all areas of the globe will be required. Thus, speed, extensibility, and flexibility requirements must be added to the accuracy constraint. To achieve these goals, a two-stage classification approach is used. The first stage uses a series of static and adaptive thresholds derived from statistical analysis of the polar scenes to reduce the set of possible classes to which a pixel may be assigned, once a cluster of classes has been selected, a neural network trained to distinguish between the classes in the cluster is used to make the ultimate classification.