Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 563651
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
DOI https://doi.org/10.1109/IGARSS.2014.6947529
Publication type Contribution in proceedings
Publication year 2014
Keyword(s) classification - kernel - low-rank - manifold clustering - sparse - unsupervised
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.

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