Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

Liu, Wei; Li, Shaozi; Lin, Xianming; Wu, Yundong; Ji, Rongrong

doi:10.1007/s00530-015-0450-0

Cited by 14 publications

(8 citation statements)

References 38 publications

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“…In the future work, more advanced features, such as deep learning-based features, can be considered to improve the performance further [32,33]. Besides, the spectral and spatial information can be fused to obtain more powerful features [34,35]. …”

Section: Resultsmentioning

confidence: 99%

Fast hyperspectral band selection based on spatial feature extraction

Cao

Wang

et al. 2018

J Real-Time Image Proc

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Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand. Band selection is an effective method to address these issues. However, most band selection algorithms are conducted with the high-dimensional band images, which will bring high computation complexity and may deteriorate the selection performance. In this paper, spatial feature extraction is used to reduce the dimensionality of band images and improve the band selection performance. The experiment results obtained on three real hyperspectral datasets confirmed that the spatial feature extraction-based approach exhibits more robust classification accuracy when compared with other methods. Besides, the proposed method can dramatically reduce the dimensionality of each band image, which makes it possible for band selection to be implemented in real time situations.

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Section: Resultsmentioning

confidence: 99%

Fast hyperspectral band selection based on spatial feature extraction

Cao

Wang

et al. 2018

J Real-Time Image Proc

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“…Thus, in this paper, we mainly focused on the clustering approach for HSI partitioning. Generally speaking, clustering techniques can be mainly categorized into nine main types [5]: centroid-based clustering [6][7][8], density-based clustering [9][10][11], probability-based clustering [12][13][14][15], bionics-based clustering [16,17], intelligent computingbased clustering [18,19], graph-based clustering [20,21], subspace clustering [22][23][24][25], deep learning-based clustering [26][27][28], and hybrid mechanism-based clustering [29,30]. Affinity propagation (AP) [31] is a centroid-based clustering method that identifies a set of data points that best represent the dataset and assigns each data point to a single exemplar.…”

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

Wang

Pan

et al. 2022

Remote Sensing

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In hyperspectral remote sensing, the clustering technique is an important issue of concern. Affinity propagation is a widely used clustering algorithm. However, the complex structure of the hyperspectral image (HSI) dataset presents challenge for the application of affinity propagation. In this paper, an improved version of affinity propagation based on complex wavelet structural similarity index and local outlier factor is proposed specifically for the HSI dataset. In the proposed algorithm, the complex wavelet structural similarity index is used to calculate the spatial similarity of HSI pixels. Meanwhile, the calculation strategy of the spatial similarity is simplified to reduce the computational complexity. The spatial similarity and the traditional spectral similarity of the HSI pixels jointly constitute the similarity matrix of affinity propagation. Furthermore, the local outlier factors are applied as weights to revise the original exemplar preferences of the affinity propagation. Finally, the modified similarity matrix and exemplar preferences are applied, and the clustering index is obtained by the traditional affinity propagation. Extensive experiments were conducted on three HSI datasets, and the results demonstrate that the proposed method can improve the performance of the traditional affinity propagation and provide competitive clustering results among the competitors.

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“…The traditional spectral clustering algorithm, however, focuses on clustering accuracy whereas ignores the underlying computational complexity and memory requirements for constructing the similarity matrix and computing eigenvectors. Therefore, it is difficult to be directly applied to large‐scale data, such as hyperspectral remote sensing image classification [10, 11]. Meanwhile, anchor graph provides a better solution.…”

Section: Introductionmentioning

confidence: 99%

“…ficult to be directly applied to large-scale data, such as hyperspectral remote sensing image classification [10,11]. Meanwhile, anchor graph provides a better solution.…”

mentioning

confidence: 99%

A fuzzy spectral clustering algorithm for hyperspectral image classification

Zhao

et al. 2021

IET Image Processing

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Spectral clustering is an unsupervised clustering algorithm, and is widely used in the field of pattern recognition and computer vision due to its good clustering performance. However, the traditional spectral clustering algorithm is not suitable for large-scale data classification, such as hyperspectral remote sensing image, because of its high computational complexity, and it is difficult to characterize the inherent uncertainty of the hyperspectral remote sensing image. This paper uses fuzzy anchors to process hyperspectral image classification and proposes a novel spectral clustering algorithm based on fuzzy similarity measure. The proposed algorithm utilizes the fuzzy similarity measure to obtain the similarity between the data points and the anchors, and then gets the similarity matrix. Finally, spectral clustering is performed on the similarity matrix to compute the classification results. The experimental results on the hyperspectral remote sensing image data sets have demonstrated the effectiveness of the proposed algorithm, and the introduction of fuzzy similarity measure gives rise to a more robust similarity matrix. Compared with existing methods, the proposed algorithm has a better classification result on the hyperspectral remote sensing image, and the kappa coefficient obtained by the proposed algorithm is 2% higher than the traditional algorithms. INTRODUCTIONWith the development of remote sensing technology, the application of hyperspectral remote sensing image has become more and more extensive. The accurately segmentation ground features through hyperspectral remote sensing image has received widespread attention. Over the past few years, unsupervised learning played an important role in the field of hyperspectral image classification because it did not need labels and did not have the problem of labelling error. As a typical method, spectral clustering algorithm [1] has been widely used in medical image analysis [2, 3], traffic forecasting [4], image segmentation [5][6][7], and other fields [8,9] due to its good performance of multi-morphological spatial clustering and convergence to the global optimal solution. The traditional spectral clustering algorithm, however, focuses on clustering accuracy whereas ignores the underlying computational complexity and memory requirements for constructing the similarity matrix and computing eigenvectors. Therefore, it is dif-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

Cited by 14 publications

References 38 publications

Fast hyperspectral band selection based on spatial feature extraction

Fast hyperspectral band selection based on spatial feature extraction

Affinity Propagation Based on Structural Similarity Index and Local Outlier Factor for Hyperspectral Image Clustering

A fuzzy spectral clustering algorithm for hyperspectral image classification

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