Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification

Sun, Weiwei; Zhang, Liangpei; Du, Bo; Li, Weiyue; Lai, Yenming Mark

doi:10.1109/jstars.2015.2417156

Cited by 161 publications

(100 citation statements)

References 47 publications

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“…We utilize five state-of-the-art methods to make holistic comparisons with our methods, including maximum-variance principal component analysis (MVPCA) [19], sparse-based band selection (SpaBS) method [36], SNMF [38], ISSC [41], and SSR [45] methods. First, we quantify the band-selection performance of DWSSR and compare the results with those of SSR.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…The sparse support vector machine (SVM) method picks important bands using a clear gap between zero and nonzero weights from the model [40]. The improved sparse subspace clustering (ISSC) method finds a band subset using spectral clustering on the similarity matrix constituted with sparse coefficient vectors [41]. The sparse CEM method regularizes the regular CEM operator with a sparse constraint and solves a convex quadratic programming problem to select the important bands [42].…”

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confidence: 99%

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A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

Sun

Zhang

et al. 2016

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

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A new dissimilarity-weighted sparse selfrepresentation (DWSSR) method has been presented to select a proper band subset for hyperspectral imagery (HSI) classification. The DWSSR assumes that all the bands can be represented by the selected band subset, and it formulates sparse representation of all the bands into a sparse self-representation (SSR) model with row-sparsity constraint in the coefficient matrix. Furthermore, the DWSSR integrates a dissimilarity-weighted regularization term with the SSR model to avoid the issue of too-close bands encountered in the SSR. The regularization term explains the encoding cost of all bands with the representative bands, and a new composite dissimilarity measure which combines spectral information divergence with intraband correlation is implemented to estimate the encoding weight. The DWSSR program is solved by the alternating direction method of multipliers (ADMM) framework, and the representative bands are finally selected according to the norm rankings of nonzero rows in the estimated coefficient matrix. Five groups of experiments on three popular HSI datasets are designed to test the performance of DWSSR in band selection, and five state-of-the-art methods are utilized to make comparisons. The results show that the DWSSR performs almost best among all the six methods, either in computational time or classification accuracies.Index Terms-Band selection, classification, dissimilarityweighted sparse self-representation (DWSSR), hyperspectral imagery (HSI).

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Section: B Experimental Resultsmentioning

confidence: 99%

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confidence: 99%

A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

Sun

Zhang

et al. 2016

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

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“…Meanwhile, the SSR assumes that all the HSI bands are sampled from a union of independent subspaces constituted from several bands, each representative band z j can then be approximately sparsely represented in the feature space spanned by all the bands [46],…”

Section: Symmetric Sparse Representation Of Hsi Bandsmentioning

confidence: 99%

“…When the dictionary in sparse coding is set to be equal to the HSI data matrix, all band vectors can be assumed to be sampled from several independent subspaces and the Sparse Subspace Clustering (SSC) model is then formulated. Typical methods include the collaborative sparse model based method [34] and the Improved Sparse Subspace Clustering (ISSC) method [46]. The SSC based methods combine the sparse coding model with the subspace clustering approach, and the benefit of clustering renders that the achieved band subset is easy to interpret.…”

Section: Introductionmentioning

confidence: 99%

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Sun

Jiang

et al. 2016

Remote Sensing

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Abstract:A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications.

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“…Clustering is a widely used method of selecting representative and diverse bands, and the cluster centers are typically the selected bands. Sun et al selected appropriate band subset using sparse subspace clustering [14]. Yuan et al proposed a dual clustering method that includes the contextual information in the clustering process and introduced a new strategy that selects the cluster representatives jointly considering the mutual effects of each cluster [15].…”

Section: Introductionmentioning

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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Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification

Cited by 161 publications

References 47 publications

A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Fast hyperspectral band selection based on spatial feature extraction

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