Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images

Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisenso...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-16
Main Authors: Huang, Shaoguang, Zhang, Hongyan, Pizurica, Aleksandra
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Clustering methods
Data acquisition
Data sources
Erbium
Feature extraction
Graph theory
Graphs
Hyperspectral images (HSIs)
Hyperspectral imaging
Image acquisition
Image classification
Image segmentation
Machine learning
multiview clustering
Optimization
Regularization
Remote sensing
Sparse matrices
Spatial data
subspace clustering
Subspaces
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Summary:Clustering algorithms play an essential and unique role in classification tasks, especially when annotated data are unavailable or very scarce. Current clustering approaches in remote sensing are mostly designed for a single data source, such as hyperspectral image (HSI), while, nowadays, multisensor data are being routinely acquired. In this article, we propose a multiview subspace clustering model that exploits effectively the rich information from multiple features extracted either from a single data source (HSI) or from multiple sources that we call generically multiviews of the same scene. An important novelty of our approach is that it integrates local and nonlocal spatial information from each view in a unified framework. Our model learns a common intrinsic cluster structure from view-specific subspace representations by a new decomposition-based scheme. In addition, we develop innovative manifold-based spatial regularization as a hybrid hypergraph, which merges local and nonlocal spatial context and improves, thereby, the learning of view-specific structures. We develop an efficient algorithm to solve the resulting optimization problem. Extensive experiments on real data sets demonstrate the superior clustering performance over the state of the art.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3074184