AN INTERACTIVE ANALYSIS METHOD FOR MULTIDIMENSIONAL IMAGES USING A HILBERT CURVE

To analyze multidimensional Images we need a mapping of feature vector s from a multidimensional space to a lower dimensional space. In gener al, these are performed using linear transformation methods, such as p rincipal component analysis, etc. Linear transformation requires many rotations of data from several points of view because the mapping is n ot one-to-one. Here, a new interactive method for classifying multispe ctral images using a Hilbert curve is presented. The Hilbert curve is a one-to-one mapping from N-dimensional space to one-dimensional space and preserves the neighborhood as much as possible. Hilbert curve is a kind of space filling curves, and provides a continuous scan. The me rit of the system presented is that the user can extract category clus ters without computing any distance in N-dimensional space easily. The method presented here is explained in brief. Clusters are extracted f rom 1-D data mapped by a Hilbert curve interactively, i.e., a pixel is classified as a category. The user can analyze multidimensional image s hierarchically from gross data distribution to fine data distributio n. To realize the real time response from the system, data tables stor ing the addresses and the occurrences of data are used. Here, the addr ess is defined by using the coordinates in N-dimensional space, and a part of mapping which cannot preserve the neighborhood is utilized. In the experiments ex-extracting categories from LANDSAT data, it is con firmed that the user can obtain the real time response from the system after once making the data tables.