Abstract:
In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of ...Show MoreMetadata
Abstract:
In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 47, Issue: 12, December 2009)