Mining of Remote Sensing Image Archives using Spatial Relationship Histograms

We describe a new image representation using spatial relationship histograms that extend our earlier work on modeling image content using attributed relational graphs. These histograms are constructed by classifying the regions in an image, computing the topological and distance-based spatial relationships between these regions, and counting the number of times different groups of regions are observed in the image. We also describe a selection algorithm that produces very compact representations by identifying the distinguishing region groups that are frequently found in a particular class of scenes but rarely exist in others. Experiments using Ikonos scenes illustrate the effectiveness of the proposed representation in retrieval of images containing complex types of scenes such as dense and sparse urban areas.