@inproceedings{59270ec728c443d392830ee48ffda5c3,
title = "Non-linear low-rank and sparse representation for hyperspectral image analysis",
abstract = "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.",
keywords = "classification, kernel, low-rank, manifold clustering, sparse, unsupervised",
author = "{De Morsier}, Frank and Devis Tuia and Maurice Borgeaucft and Volker Gass and Thiran, {Jean Philippe}",
year = "2014",
month = nov,
day = "4",
doi = "10.1109/IGARSS.2014.6947529",
language = "English",
isbn = "9781479957750",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "4648--4651",
booktitle = "International Geoscience and Remote Sensing Symposium (IGARSS)",
address = "United States",
note = "Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",
}