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An Efficient Gabor Feature-Based Multi-task Joint Support Vector Machines Framework for Hyperspectral Image Classification

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

In this paper, a novel multi-task learning (MTL) framework for a series of Gabor features via joint probabilistic outputs of support vector machines (SVM), abbreviated as GF-MTJSVM, has been proposed for Hyperspectral image (HSI) classification. Specifically, we firstly use a series of Gabor wavelet filters with different scales and frequencies to extract spectral-spatial-combined features from the HSI data. Then, we apply these Gabor features into the multi-task learning framework via joint probabilistic outputs of SVM. Experimental results on two widely used real HSI data indicate that the proposed GF-MTJSVM approach outperforms several well-known classification methods.

This work was jointly supported by grants from National Natural Science Foundation of China (61671307 and 61271022), Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions (Yq2013143), Shenzhen Scientific Research and Development Funding Program (JCYJ20140418095735628, JCYJ20160422093647889 and SGLH20150206152559032).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Sen Jia & Bin Deng

Authors
  1. Sen Jia
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  2. Bin Deng
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Corresponding authors

Correspondence to Sen Jia or Bin Deng .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Jia, S., Deng, B. (2016). An Efficient Gabor Feature-Based Multi-task Joint Support Vector Machines Framework for Hyperspectral Image Classification. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_2

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_2

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  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

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