A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

Yuwei Liao; Deovrat Kakde; Arin Chaudhuri; Hansi Jiang; Carol Sadek; Seunghyun Kong

doi:10.1117/12.2314964

8 May 2018 A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

Yuwei Liao, Deovrat Kakde, Arin Chaudhuri, Hansi Jiang, Carol Sadek, Seunghyun Kong

Proceedings Volume 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV; 106441M (2018) https://doi.org/10.1117/12.2314964
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. This paper proposes a new automatic, unsupervised Gaussian kernel bandwidth selection approach which is used with a multiclass SVDD classification scheme. The performance of the multiclass SVDD classification scheme is evaluated on three frequently used hyperspectral data sets, and preliminary results show that the proposed method can achieve better performance than published results on these data sets.

Citation Download Citation

Yuwei Liao, Deovrat Kakde, Arin Chaudhuri, Hansi Jiang, Carol Sadek, and Seunghyun Kong "A new bandwidth selection criterion for using SVDD to analyze hyperspectral data", Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106441M (8 May 2018); https://doi.org/10.1117/12.2314964

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