Knowledge Discovery in an Oceanographic Database

Knowledge discovery from image data is a multi-step iterative process. This paper describes the procedure we have used to develop a knowledge discovery system that classifies regions of the ocean floor based on textural features extracted from acoustic imagery. The image is subdivided into rectangular cells called texture elements (texels); a gray-level co-occurence matrix (GLCM) is computed for each texel in four directions. Secondary texture features are then computed from the GLCM resulting in a feature vector representation of each texel instance. Alternatively, a region-growing approach is used to identify irregularly shaped regions of varying size which have a homogenous texture and for which the texture features are computed. The Bayesian classifier Autoclass is used to cluster the instances. Feature extraction is one of the major tasks in knowledge discovery from images. The initial goal of this research was to identify regions of the image characterized by sand waves. Experiments were designed to use expert judgements to select the most effective set of features, to identify the best texel size, and to determine the number of meaningful classes in the data. The region-growing approach has proven to be more successful than the texel-based approach. This method provides a fast and accurate method for identifying provinces in the ocean floor of interest to geologists.