Unsupervised classification plays a key role in remote sensing hyperspectral image analysis. Complexities arise from the high dimensionality of hyperspectral imagery and this implies the need for dimensionality reduction as a vital preprocessing step. However, conventional dimensionality reduction techniques, such as linear and nonlinear manifold learning approaches, may fail if the hyperspectral remote sensing data stem from several intersecting data manifolds. In this paper, we consider remote sensing hyperspectral data within the framework of multi-manifold learning with possible intersections. To this end, we propose a multi-manifold spectral clustering algorithm for unsupervised classification of hyperspectral imagery. The proposed algorithm exploits the notion of shared nearest neighbourhood for the construction of nearest neighbour connectivity model and a weighted principal component analysis model for tangent space estimation. Preliminary results on two benchmark hyperspectral data sets reveal the superiority of the proposed algorithm in terms of clustering accuracy over approaches based on conventional dimensionality reduction techniques.
['Aidin Hassanzadeh', 'Tuomo Kauranne', 'Arto Kaarna']
A multi-manifold clustering algorithm for hyperspectral remote sensing imagery