An Efficient Algorithm for Hyperspectral Image Clustering

Pan, Yushu; Jiao, Yuchen; Li, Tiejian; Gu, Yuantao

doi:10.1109/icassp.2019.8683309

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…In the experiment, we remove 20 spectral bands in 104-108, 150-163, and 200 due to water absorption. For computational efficiency, a typical subimage with the spatial size 85 × 70 is tested, as in [30], [33], and [61], which includes four classes. The false-color composite image and ground truth are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images

Zhang

Pižurica

2022

IEEE Trans. Geosci. Remote Sensing

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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 viewspecific 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.

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Section: Methodsmentioning

confidence: 99%

Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images

Zhang

Pižurica

2022

IEEE Trans. Geosci. Remote Sensing

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“…To create an affinity matrix, the connectivity of each node must be expressed by a connectivity graph G. G is implicitly constructed from graph kernels K. The kernel weights, elements of K, manifest the connectivity between a node and its neighbor. The method introduces a statistical sub-graph affinity model to substitute the node affinity model [38]. Instead of comparing x u to x v for v in the neighbor of u, the sub-graph P u is compared against each sub-graph P v , which is illustrated in Figure 1.…”

Section: Methodology Ssakgcscmentioning

confidence: 99%

“…The kernel weights, elements of K , manifest the connectivity between a node and its neighbor. The method introduces a statistical sub-graph affinity model to substitute the node affinity model [38]. Instead of comparing u…”

Section: XX Xx Zmentioning

confidence: 99%

Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model

Lin¹,

Wang²,

Wu³

2022

Axioms

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Hyperspectral image (HSI) clustering is a challenging work due to its high complexity. Subspace clustering has been proven to successfully excavate the intrinsic relationships between data points, while traditional subspace clustering methods ignore the inherent structural information between data points. This study uses graph convolutional subspace clustering (GCSC) for robust HSI clustering. The model remaps the self-expression of the data to non-Euclidean domains, which can generate a robust graph embedding dictionary. The EKGCSC model can achieve a globally optimal closed-form solution by using a subspace clustering model with the Frobenius norm and a Gaussian kernel function, making it easier to implement, train, and apply. However, the presence of noise can have a noteworthy negative impact on the segmentation performance. To diminish the impact of image noise, the concept of sub-graph affinity is introduced, where each node in the primary graph is modeled as a sub-graph describing the neighborhood around the node. A statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty image noise by using more information. The model used in this work was named statistical sub-graph affinity kernel graph convolutional subspace clustering (SSAKGCSC). Experiment results on Salinas, Indian Pines, Pavia Center, and Pavia University data sets showed that the SSAKGCSC model can achieve improved segmentation performance and better noise resistance ability.

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“…Multi-scale features possess the capability to capture object representations at various scales in images, showcasing excellent performance across multiple tasks [21][22][23][24][25][26][27]. NAS-FPN [28] employs reinforcement learning to train a controller that identifies the optimal model architectures within a predefined search space.…”

Section: Multi-scale Feature Fusionmentioning

confidence: 99%

Preface: Celebrating the 100th anniversary of chemical sciences in Nanjing University

Wang

2020

Sci. China Chem.

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The generalized weak Galerkin (gWG) finite element method is proposed and analyzed for the biharmonic equation. A new generalized discrete weak second order partial derivative is introduced in the gWG scheme to allow arbitrary combinations of piecewise polynomial functions defined in the interior and on the boundary of general polygonal or polyhedral elements. The error estimates are established for the numerical approximation in a discrete H 2 norm and a L 2 norm. The numerical results are reported to demonstrate the accuracy and flexibility of our proposed gWG method for the biharmonic equation.

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An Efficient Algorithm for Hyperspectral Image Clustering

Cited by 8 publications

References 16 publications

Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images

Hybrid-Hypergraph Regularized Multiview Subspace Clustering for Hyperspectral Images

Robust Spectral Clustering Incorporating Statistical Sub-Graph Affinity Model

Preface: Celebrating the 100th anniversary of chemical sciences in Nanjing University

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