Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data

Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-reso...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 25; no. 1; pp. 274 - 288
Main Authors:	Veganzones, Miguel A., Simoes, Miguel, Licciardi, Giorgio, Yokoya, Naoto, Bioucas-Dias, Jose M., Chanussot, Jocelyn
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2016 Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Bands binary partition tree Computer Science data fusion dictionary learning Hyperspectral imagery Hyperspectral imaging Ill posed problems Manifolds multispectral imagery Partitions Sensors Signal and Image Processing Signal resolution Spatial resolution spectral unmixing Subspaces super-resolution data fusion spectral unmixing Hyperspectral imagery super-resolution multispectral imagery binary partition tree dictionary learning spectral un-mixing
ISSN:	1057-7149, 1941-0042
Online Access:	Get full text
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