A unified unsupervised clustering algorithm and its first application to landcover classification
The problem of classification is so fundamental that it has been intensively investigated by many researchers from different domains. In this paper, we present a novel unsupervised clustering algorithm derived from the techniques of probabilistic modeling which is implemented by a stochastic gradient algorithm. Then its application to challenging landcover classification based on Daedalus data of the SMART project is explored by combining both spectral feature and spatial contextual information. Our first experiments show its potential usefulness in remote sensing.