Spatial-Spectral Clustering With Anchor Graph for Hyperspectral Image

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters without labeled training data, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, h...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-13
Main Authors: Wang, Qi, Miao, Yanling, Chen, Mulin, Yuan, Yuan
Format: Article
Language:English
Subjects:
Anchor graph (AG)
Clustering
Clustering algorithms
Clustering methods
Computer applications
Correlation
Decomposition
Eigenvalues
Eigenvalues and eigenfunctions
graph-based clustering
hyperspectral image (HSI)
Hyperspectral imaging
Linear programming
Optimization
Pixels
Similarity
Singular value decomposition
spatial-spectral information
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Summary:Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters without labeled training data, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. To better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the multiscale filtering module is utilized to smooth the homogeneous regions, so that it can increase the similarity and consistency of neighboring pixels and capture the multiple views of a local region with different scales; 2) a new similarity metric is proposed to embed the spatial-spectral features into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; and 3) the anchor graph (AG)-based strategy is adopted to construct the adjacent graph by a neighbor assignment scheme without hyperparameters, and its optimization employs singular value decomposition (SVD) to replace eigenvalue decomposition to reduce the computational complexity. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3217597