Scale Space Based Hierarchical Clustering Method and its Application to Remotely Sensed Data Classification
LUO Jian cheng 1, YEE Leung 2, ZHOU Cheng hu 1 (1. LREIS,Institute of Geography, CAS, Beijing, 100101; 2. Department of Geography, The Chinese University of Hong Kong,Hong Kong)
In Pattern recognition and image processing, the major application areas of cluster analysis, human eyes seem to posses a singular aptitute to group objects and find important structures in an efficient and effective way. Scale Space Based Hierarchical Clustering Method (SSHC) posses a nonlinear dynamical mechanism which simulates the human vision system in relaxing process to a object from legible extend to all blurring extend. The main advantages of SSHC method are: (1) it's distribution of feature space could be assumed free, (2) by FFE decision rules the finest clustering centers and number can be easily extracted, (3) the outside knowledge can be integrated with the process of clustering fusion. In this article,the SSHC's thermodynamic and simulating vision mechanism is analyzed at first, then the SSHC algorithm and the procedure of making the clustering tree are described in detail, in which the decision rule how to acquire the finest clustering centers from clustering tree by Fractional Free Energy (FFE) of each fusion point and the points number of leaf nodes under which the son-tree owns is presented out. At last, we present out the framework of SSHC based on a case of Remotely Sensed Data Classification. We attain such a result that SSHC based RS classification method holds more practicality and flexibility than others RS Clustering Methods.
【CateGory Index】： P237.7