A Mini-Raster Data Structure for Representing and Reasoning with Spatial Objects in Satellite Images
This paper describes the design and implementation of a spatial data structure, called the "mini-raster", which uses a raster-based technique to represent individual objects depicted in satellite images. An image is divided into spatial objects using an appropriate segmentation technique; then each object is represented by its own miniature raster. This mini-raster is created by extracting that part of the segmented image contained within the bounding rectangle of the object and using it to create a binary mask defining the precise spatial extent of the object, with a 1 representing a pixel within the bounding rectangle which belongs to the specified object and a 0 representing a pixel which does not. The mini-raster, together with other relevant details relating to the object, is stored in working memory, in a structure resembling the frame data structure commonly used in knowledge-based systems.
The mini-rasters store full spatial information about all defined objects within the image in a way that allows us to determine individual object attributes (such as area, perimeter and shape characteristics) and to establish relationships (such as containment and adjacency) between small groups of objects, as required. Moreover, it enables this information to be retained in a raster form. The data structure is less efficient, in terms of storage space, than a vector structure or more sophisticated raster-like structures such as quadtrees, but the fact that it can be stored and manipulated as a simple two-dimensional binary array has very significant computational advantages.