The Fast Spectral Clustering Based on Spatial Information for Large Scale Hyperspectral Image

Wei, Yiwei; Niu, Chao; Wang, Yiting; Wang, Hongxia; Liu, Daizhi

doi:10.1109/access.2019.2942923

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…To handle the defect that AG-based methods usually ignore the spatial information, Wang et al [47] presented a AG-based method with clustering for HSI data, which fuses the spatial information by using the mean of neighboring pixels to reconstruct center pixel. Wei et al [48] utilized the spatial correlation by adopting weighted mean filtering (WMF) to filter hyperspectral pixels, which considers the local neighborhood relationship within a window. He et al [23] presented semi-supervised model with bipartite graph, which calculates the labels of the data and anchors simultaneously and adopts the Woodbury matrix to handle the large matrix inverse.…”

Section: B Ag-based Hsi Classification Methodsmentioning

confidence: 99%

Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning

Miao

Chen

Yuan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semi-supervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limited labeled data for classification task. However, it is hard to construct the pairwise adjacent graph due to the high dimensionality of hyperspectral data. Besides, the graph-based SSL models are usually decided by a single classifier, which fail to effectively learn the complex structures and intrinsic properties of HSI. To address these problems, we propose a novel graph-based SSL classification model for HSI, which is based on Random Multi-Graphs construction and Ensemble strategy (RMGE). Specifically, the anchor graph (AG) is constructed with spatial-spectral features, which integrates the spatial characteristics extracted by local binary pattern (LBP) on each selected spectrum, preserving fine structures of local region. In order to enhance the discriminative capability of the classifier and avoid the trivial solution, the maximum entropy regularization is added into adjacent AG model. In addition, to capture the diversity of HSI data effectively, we design the ensemble framework by employing multiple AGs to learn HSI features. Experiments conducted on real hyperspectral datasets indicate that the proposed RMGE shows better performance than that of state-of-the-art approaches.

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Section: B Ag-based Hsi Classification Methodsmentioning

confidence: 99%

Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning

Miao

Chen

Yuan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, spectral clustering has become one of the most popular modern clustering approaches with a huge number of variants being developed, whose main tools are the graph Laplacian matrices, including normalized and unnormalized graph Laplacian [1], [30]- [32]. To improve the performance of spectral clustering, two aspects have been considered by researchers.…”

Section: A Graph-based Clustering Approachesmentioning

confidence: 99%

Graph-Based Clustering via Group Sparsity and Manifold Regularization

2019

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Clustering refers to the problem of partitioning data into several groups according to the predefined criterion. Graph-based method is one of main clustering approaches and has been shown impressive performance in many literatures. The core issue of graph-based clustering is how to construct a good adjacency graph. A large number of works employ the sparse representation of data as the similarity measure by 1 regularization. However, due to the flat nature of the 1 norm, such methods solve the sparse representation of each data point individually, which do not take into account the global structure of data. To exploit the global and essential structure in data, in contrast to existing methods, we propose to learn a graph with group sparsity. To incorporate more information into the graph, we also use the manifold regularization with adaptive similarity during the process of group sparse self-representation. The resulting model is handled by Alternating Direction Method of Multipliers (ADMM). Further, we employ Iterative Re-weighted Least Squares (IRLS) algorithm and threshold operator to solve the ADMM subproblems. Experimental results on real-world datasets demonstrate the superiority of our method compared to the competing clustering methods. INDEX TERMS Clustering, graph, group sparsity.

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“…b) AG-based clustering methods: SGCNR [18], FSCAG [32] and FSCS [33]. The first method SGCNR only adopts the spectral feature, while other methods FSCAG and FSCS consider spatial and spectral information.…”

Section: B Experimental Setup 1) Comparison Algorithmsmentioning

confidence: 99%

Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images

Wang

Nie

2017

IEEE Geosci. Remote Sensing Lett.

111

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Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graphbased 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. In order 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 anchor graph-based strategy is used to construct a tractable large graph for HSI data, which effectively exploits all data points and reduces the computational complexity; 2) a new similarity metric is presented to embed the spatial-spectral information into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; 3) an effective neighbors assignment strategy is adopted in the optimization, which performs the singular value decomposition (SVD) on the adjacent graph to get solutions efficiently. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.

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The Fast Spectral Clustering Based on Spatial Information for Large Scale Hyperspectral Image

Cited by 14 publications

References 23 publications

Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning

Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning

Graph-Based Clustering via Group Sparsity and Manifold Regularization

Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images

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