Graph-Induced Aligned Learning on Subspaces for Hyperspectral and Multispectral Data

In this article, we have great interest in investigating a common but practical issue in remote sensing (RS)-can a limited amount of one information-rich (or high-quality) data, e.g., hyperspectral (HS) image, improve the performance of a classification task using a large amount of another informati...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 59; číslo 5; s. 4407 - 4418
Hlavní autori:	Hong, Danfeng, Kang, Jian, Yokoya, Naoto, Chanussot, Jocelyn
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	Cross-modality Data Engineering Sciences fusion graph learning hyperspectral (HS) Hyperspectral imaging Image classification Image quality Kernel Labels Learning Machine learning Manifolds multispectral (MS) Optimization pseudo-labels Remote sensing remote sensing (RS) semisupervised Signal and Image processing subspace learning Subspaces Task analysis Training subspace learning fusion Cross-modality multispectral (MS) semisupervised hyperspectral (HS) remote sensing (RS) graph learning pseudo-labels
ISSN:	0196-2892, 1558-0644
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