Hierarchical scene understanding exploiting automatically derived contextual data

In this paper we present methods for scene understanding, localization and classification of complex, visually heterogeneous objects from overhead imagery. Key features of this work include: determining boundaries of objects within large field-of-view images, classification of increasingly complex object classes through hierarchical descriptions, and exploiting automatically extracted hypotheses about the surrounding region to improve classification of a more localized region. Our system uses a principled probabilistic approach to classify increasingly larger and more complex regions, and then iteratively uses this automatically determined contextual information to reduce false alarms and misclassifications.