New PDF release: Advances in Neural Networks – ISNN 2016: 13th International

By Long Cheng, Qingshan Liu, Andrey Ronzhin

ISBN-10: 3319406620

ISBN-13: 9783319406626

ISBN-10: 3319406639

ISBN-13: 9783319406633

This ebook constitutes the refereed lawsuits of the thirteenth foreign Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016. The eighty four revised complete papers offered during this quantity have been conscientiously reviewed and chosen from 104 submissions. The papers conceal many issues of neural network-related learn together with sign and picture processing; dynamical behaviors of recurrent neural networks; clever keep watch over; clustering, type, modeling, and forecasting; evolutionary computation; and cognition computation and spiking neural networks.

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Extra info for Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings

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ISNN 2016, LNCS 9719, pp. 21–29, 2016. 1007/978-3-319-40663-3_3 22 M. Han et al. analysis (LDA) seem to be the most typical feature extraction algorithms [6]. However, recent research has shown that the hyperspectral dataset is likely to lie in a nonlinear submanifold, which leads to linear algorithms inefficiency [7–9]. To deal with the problem, manifold learning-based feature extraction methods have been developed, which can explicitly discover the nonlinear manifold structure concealed in original high dimensional dataset, such as locality preserving projections (LPP) and neighborhood preserving embedding (NPE) [10, 11].

It is an almost perfect classification map. 4 Conclusions In this paper, a framework is proposed to improve the classification accuracy of hyper spectral image, including spatial information extraction by modified bilateral filter, dimensionality reduction by the proposed feature extraction algorithm LPDA, image classification by SVMCK. The experiment results have proven that the incorporation of spatial information can significantly improve the smoothness of the classification map. The experimented NWFE, LFDA, NPE and LPDA all produce better results than that without this preprocessing step.

1. Classification framework used in this research. 1 23 Spatial Information Extraction Spatial filtering is an effective approach to extract spatial information. Bilateral filter takes spatial-domain and radiant-domain filtering into consideration, and can achieve smooth neighborhoods and distinct edges [15]. It can be represented mathematically: Z hðzÞ ¼ 1=kðzÞ f ðxÞ Á cðx; zÞ Á uðf ðxÞ; f ðzÞÞdx X Z kðzÞ ¼ cðx; zÞuðf ðxÞ; f ðzÞÞdx ð1Þ X where z is a central pixel surrounded by neighboring pixels x in a spatial domain X, kðzÞ is the normalization factor.

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Advances in Neural Networks – ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings by Long Cheng, Qingshan Liu, Andrey Ronzhin


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