Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images

Zhang, Hongyan; Pižurica, Aleksandra

doi:10.1109/igarss.2019.8898869

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…For large scale HSIs with millions of pixels in each band, this bound can thus exceed 10 18 elementary operations per iteration, and such processing becomes often infeasible on the common computing platforms. The approaches reported in References [26,27] addressed this problem by constructing a graph based on a set of selected representative samples. In combination with modified spectral clustering methods, a lower complexity has been reached, but the clustering results are sensitive to the initially selected samples.…”

Section: Introductionmentioning

confidence: 99%

Sketch-Based Subspace Clustering of Hyperspectral Images

Zhang

Pižurica

2020

Remote Sensing

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Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images.

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Section: Introductionmentioning

confidence: 99%

Sketch-Based Subspace Clustering of Hyperspectral Images

Zhang

Pižurica

2020

Remote Sensing

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“…Recent works [69][70][71][72][73][74][75][76][77][78][79] solve this problem by replacing the self-representation dictionary with a more compact dictionary. Typical ways to obtain the compact dictionary are shown in Figure 7.…”

Section: Dictionary Learning Based Clustering Methodsmentioning

confidence: 99%

“…Landmark based JSCC [69], LSSC-TV [70], SC-SSC [71], MOMSSC-L0-TV [72] Computationally efficient clustering methods due to the adopted landmark dictionaries.…”

Section: Dictionary Learning Basedmentioning

confidence: 99%

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

2023

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Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.

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“…Sparse representation-based improved subspace clustering considers both sparseness and grouping effect [15]. SSC model with TV regularization (LSSC-TV) accelerates the clustering speed of traditional SSC algorithm and uses the total variation property of images for obtaining strong robustness to noise [16]. Structured sparse relation representation-based subspace clustering proposes the idea of relation reconstruction, that indicates the true membership by neighbourhood relation, and gives a much different way to define affinity graphs [17].…”

Section: Introductionmentioning

confidence: 99%

Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm

Liao

Liu

Xie

2022

J. Phys.: Conf. Ser.

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Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.

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Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images

Cited by 4 publications

References 14 publications

Sketch-Based Subspace Clustering of Hyperspectral Images

Sketch-Based Subspace Clustering of Hyperspectral Images

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm

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