Pattern recognition with measurement space and spatial clustering for multiple images

Abstract

Remote sensor imaging technology makes it possible to obtain multiple images of extensive land areas simultaneously from the radar, infrared, and visible portions of the electromagnetic spectrum. It would be useful to automatically obtain from such data land-use maps indicating those areas of similar types of land, that is, similar as seen through the sensor's eyes. This classification problem is approached from the perspective of the structure inherent in the data. The classification categories or clusters so constructed are the natural homogeneous groupings within the data. There is high similarity within each cluster and high dissimilarity between clusters. Two clustering procedures are presented: the first partitions the image sequence and the second partitions the measurement space. In both, the partition is constructed by finding appropriate center sets and then chaining to them all similar enough points. The resulting clusters are simply connected and not necessarily convex. An example of the measurement space clustering procedure is presented for a set of three multispectral images taken over Phoenix, Ariz.