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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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 59; H. 5; S. 4407 - 4418
Hauptverfasser: Hong, Danfeng, Kang, Jian, Yokoya, Naoto, Chanussot, Jocelyn
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
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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Zusammenfassung: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 information-poor (low-quality) data, e.g., multispectral (MS) image? This question leads to a typical cross-modality feature learning. However, classic cross-modality representation learning approaches, e.g., manifold alignment, remain limited in effectively and efficiently handling such problems that the data from high-quality modality are largely absent. For this reason, we propose a novel graph-induced aligned learning (GiAL) framework by 1) adaptively learning a unified graph (further yielding a Laplacian matrix) from the data in order to align multimodality data (MS-HS data) into a latent shared subspace; 2) simultaneously modeling two regression behaviors with respect to labels and pseudo-labels under a multitask learning paradigm; and 3) dramatically updating the pseudo-labels according to the learned graph and refeeding the latest pseudo-labels into model learning of the next round. In addition, an optimization framework based on the alternating direction method of multipliers (ADMMs) is devised to solve the proposed GiAL model. Extensive experiments are conducted on two MS-HS RS data sets, demonstrating the superiority of the proposed GiAL compared with several state-of-the-art methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3021140