CASSIE: Contextual Analysis for Spectral and Spatial Information Extraction

Advances in understanding the biology of vision show that humans use not only bottom-up, feature-based information in visual analysis, but also top-down contextual information. To reflect this method of processing, we developed a technology called CASSIE for Science Applications International Corporation (SAIC) that uses low-level image features and contextual cues to determine the likelihood that a certain target will be found in a given area. CASSIE is a tool by which information from various data layers can be probabilistically combined to determine spatial and informational context within and across different types of data. It is built on a spatial foundation consisting of a two-dimensional hexagonal, hierarchical grid structure for data storage and access. This same structure facilitates very fast computation of information throughout the hierarchy for all data layers, as well as fast propagation of probabilistic information derived from those layers. Our research with CASSIE investigates the effectiveness of generated probability maps to reflect a human interpretation, potential benefits in terms of accuracy and processing speed for subsequent target detection, and methods for incorporating feedback from target detection algorithms to apply additional contextual constraints (for example, allowable or expected target groupings). We discuss further developments such as learning in CASSIE and how to incorporate additional data modalities.