Cartographic indexing into a database of remotely sensed images

This paper aims to develop simple statistical methods for indexing line patterns. The application vehicle used in this study involves indexing into an aerial image database using a cartographic model. The images contained in the database are of urban and semi-urban areas. The cartographic model represents a road network known to appear in a subset of the images contained within the database. There are known to be severe imaging distortions present and the data cannot be recovered by applying a simple Euclidean transform to the model. We e ect the cartographic indexing into the database using pairwise histograms of the angle differences and the cross ratios of the lengths of line segments extracted from the raw aerial images. We investigate several alternative ways of performing histogram comparison. Our conclusion is that the Matusita and Bhattacharyya distances provide signi cant performance advantages over the L2 norm employed by Swain and Ballard. Moreover, a sensitivity analysis reveals that the angle-di erence histogram provides the most discriminating index of line-structure; it is robust both to image distortion an to the variable quality of input line-segmentation.