A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification

Kuo, Bor‐Chen; Ho, Hsin-Hua; Li, Cheng‐Hsuan; Hung, Chih‐Cheng; Taur, J.S.

doi:10.1109/jstars.2013.2262926

Cited by 326 publications

(79 citation statements)

References 53 publications

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“…Another discriminant-based supervised hyperspectral feature extraction method, called non-parametric weighted feature extraction (NWFE) (Kuo and Landgrebe 2004), uses an improved discrimination criterion by assigning a higher weight to samples closer to the discrimination boundary region. Kuo et al (2014) proposed a kernel-based hyperspectral feature selection method, which optimizes the linear combination of z-score values of features in the radial basis function kernel. In the work of Yang et al (2014), an approach inspired by compressive sensing is given which is based on a single-layer feed-forward neural network with sparsity constraints on the input and hidden layer.…”

Section: Supervised Methodsmentioning

confidence: 99%

“…Various feature extraction methods for the purpose of HSI classification have been proposed in the literature, including unsupervised methods (Hotelling 1933;Chang et al 1999;Wang and Chang 2006;Kaewpijit, Le-Moigne, and El-Ghazawi 2003;Tenenbaum, de Silva, and Langford 2000;Roweis and Saul 2000;Belkin and Niyogi 2001;Zhang and Zha 2005;He et al 2005;He and Niyogi 2004;Zhang et al 2007;Qiao, Chen, and Tan 2010), semisupervised (Cai, He, and Han 2007;Chen and Zhang 2011;Sugiyama et al 2010;Liao et al 2013;Shao and Zhang 2014), and supervised methods (Bandos, Bruzzone, and Camps-Valls. 2009;Li and Qian 2011;Kuo and Landgrebe 2004;Kuo et al 2014;Yang et al 2014;Zhong, Lin, and Zhang 2014;Tuia et al 2014;Tao et al 2013;Sugiyama 2007;Chen et al 2014;Chen, Zhao, and Jia 2015;Sun et al 2014;Castrodad et al 2011). …”

Section: Related Workmentioning

confidence: 99%

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Unsupervised spectral sub-feature learning for hyperspectral image classification

Slavkovikj

Verstockt

Neve

et al. 2016

International Journal of Remote Sensing

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Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of subfeature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods. ARTICLE HISTORY

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Section: Supervised Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Unsupervised spectral sub-feature learning for hyperspectral image classification

Slavkovikj

Verstockt

Neve

et al. 2016

International Journal of Remote Sensing

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“…SVM is a machine learning algorithm based on statistical analysis [21]. It has great advantages in solving nonlinear, small sample and high-dimensional pattern recognition based on the principle of minimizing structural risk [22].…”

Section: Prediction Principle Of Avc-svmmentioning

confidence: 99%

“…It has great advantages in solving nonlinear, small sample and high-dimensional pattern recognition based on the principle of minimizing structural risk [22]. In addition, SVM algorithm also has many applications in bioinformatics [4, 21, 22]. In this paper, the SVM algorithm was used to predict ion channel types of the conotoxins.…”

Section: Prediction Principle Of Avc-svmmentioning

confidence: 99%

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

Wang

Junmei

Wang

et al. 2017

BioMed Research International

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The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

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“…Supervised BS methods require some priori knowledge such as training samples or target signatures, for instance,91011121314. However, these training samples are sometimes not available in practice since the acquisition of reliable samples is very expensive in terms of both time and money1516.…”

mentioning

confidence: 99%

Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection

Geng

Sun

et al. 2015

Sci Rep

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Few band selection methods are specially designed for small target detection. It is well known that the information of small targets is most likely contained in non-Gaussian bands, where small targets are more easily separated from the background. On the other hand, correlation of band set also plays an important role in the small target detection. When the selected bands are highly correlated, it will be unbeneficial for the subsequent detection. However, the existing non-Gaussianity-based band selection methods have not taken the correlation of bands into account, which generally result in high correlation of obtained bands. In this paper, combining the third-order (third-order tensor) and second-order (correlation) statistics of bands, we define a new concept, named joint skewness, for multivariate data. Moreover, we also propose an easy-to-implement approach to estimate this index based on high-order singular value decomposition (HOSVD). Based on the definition of joint skewness, we present an unsupervised band selection for small target detection for hyperspectral data, named joint skewness band selection (JSBS). The evaluation results demonstrate that the bands selected by JSBS are very effective in terms of small target detection.

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A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification

Cited by 326 publications

References 53 publications

Unsupervised spectral sub-feature learning for hyperspectral image classification

Unsupervised spectral sub-feature learning for hyperspectral image classification

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection

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