|Title||Non-linear low-rank and sparse representation for hyperspectral image analysis|
|Author(s)||Morsier, Frank De; Tuia, Devis; Borgeaucft, Maurice; Gass, Volker; Thiran, Jean Philippe|
|Source||In: International Geoscience and Remote Sensing Symposium (IGARSS). - Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781479957750 - p. 4648 - 4651.|
|Event||Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, 2014-07-13/2014-07-18|
|Publication type||Contribution in proceedings|
|Keyword(s)||classification - kernel - low-rank - manifold clustering - sparse - unsupervised|
In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the pixels to be expressed as a low-rank and sparse combination of the others in a reproducing kernel Hilbert spaces (RKHS). This captures the global (low-rank) and local (sparse) structures. Spectral clustering is applied on the graph to assign the pixels to the different manifolds. A large scale approach is proposed, in which the optimization is first performed on a subset of the data and then it is applied to the whole image using a non-linear collaborative representation respecting the manifolds structure. Experiments on two hyperspectral images show very good unsupervised classification results compared to competitive approaches.