Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

Zhu, Guangtian; Huang, Yuancheng; Lei, Jingsheng; Bi, Zhongqin; Xu, Feifei

doi:10.1109/tgrs.2015.2453362

Cited by 95 publications

(39 citation statements)

References 46 publications

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“…According to the involvement of the labeled and the unlabeled samples, band selection can be divided into supervised [9][10][11][12][13], semi-supervised [14][15][16][17] and unsupervised [18][19][20][21][22][23] methods. Supervised and semi-supervised methods utilize the labeled samples to guide the selection process.…”

Section: Introductionmentioning

confidence: 99%

Optimal Clustering Framework for Hyperspectral Band Selection

Wang

Zhang

2018

IEEE Trans. Geosci. Remote Sensing

235

132

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Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets.

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Section: Introductionmentioning

confidence: 99%

Optimal Clustering Framework for Hyperspectral Band Selection

Wang

Zhang

2018

IEEE Trans. Geosci. Remote Sensing

235

132

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“…It is completely different from existing BS methods, with the following contributions: (i) It is a BSS method particularly developed for HSIC; (ii) It is quite different from single band-constrained methods in [26] and multiple-band constrained methods in [68], by constraining multiple class signature vectors instead of multiple bands; (iii) It develops three numerical search algorithms to find optimal band subsets which are different from the graph-based approaches [40,43] used by other SMMBS methods; (iv) It is very simple to implement via (7) with no parameters needing to be tuned; (v) Most importantly, it shows that HSIC can be improved by BS provided that the number n BS of selected bands and the set of n BS bands are properly selected.…”

Section: Discussionmentioning

confidence: 99%

“…Then, a search algorithm called mixture determinantal point process (Mix-DPP) was further developed to find a diverse subset that can be a potential optimal band combination. The other is DSEBS, which exploits structure information via a set of local spatial-spectral filters and uses a graph-based clustering search strategy derived from dominant set extraction to find a potential optimal band subset [40].…”

Section: This Is Infeasible Ifmentioning

confidence: 99%

Band Subset Selection for Hyperspectral Image Classification

Song

Chang

2018

Remote Sensing

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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

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“…Feature selection means selecting part of the original bands based on proper criterion [19]. Typical feature selection algorithms include multitask sparsity pursuit [20], structure-aware [21], support vector machine [22], hypergraph model [23], sparse Hilbert-Schmidt independence criterion [24] and nonhomogeneous hidden Markov chain model [25]. Different measures were used to select preferred bands, including mutual information [12], information divergence [13], variance [26] and local spatial information [27].…”

Section: Introductionmentioning

confidence: 99%

Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

Hsieh

Kiang

2020

Sensors

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Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.

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Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

Cited by 95 publications

References 46 publications

Optimal Clustering Framework for Hyperspectral Band Selection

Optimal Clustering Framework for Hyperspectral Band Selection

Band Subset Selection for Hyperspectral Image Classification

Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

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