Interactive analysis of multi-spectral images using a Hilbert curve
Sei‐ichiro Kamata, Michiharu Niimi, Eiji Kawaguchi · 2002
There have been many new developments in interactive analysis for multi-spectral images in the research of remote sensing. In general, the methods used are linear transformations such as principal component analysis. In this paper, the authors present a new interactive method for classifying multi-spectral images using a Hilbert curve which is a one-to-one mapping and preserves the neighborhood as much as possible. This method is based on a hierarchical histogram expression with different resolutions for the mapped one-dimensional data. The classification on this expression can be performed easily instead of using N-dimensional data directly. In order to realize the real time response from the system, the authors make use of data tables storing the addresses and the occurrences of data, etc. Here the address is defined by using the coordinates in N-dimensional space, and is made use of dealing with a part of mapping which can not preserve the neighborhood. In the experiments using LANDSAT image data, it is confirmed that the user can get the real time response from the system after once making the data tables.