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Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine

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Abstract

Accurate hyperspectral image classification requires not only image features but also semantic concept. Similarity and relevance relation are both key factors in building image features and semantic measurement. To perform hyperspectral image classification from the viewpoint of semantic, this study focuses on creating a semantic annotation-based image classification method with relevance and similarity measurement. First, the computational model of relevance vector machine is utilized to perform cluster computation for hyperspectral image data. Then multi-distance learning algorithm is optimized as holding capability for multiple dimensions data. The proposed multi-distance learning algorithm with multiple dimensions is used to measure the similarity, according to the result of cluster computation through relevance vector machine. Finally, semantic annotation is introduced to complete classification of hyperspectral image with semantic concept. Validation with the ground truth data illustrates that the proposed method can provide more accurate and integrated classification result compared with the other methodologies. Therefore, the integration of similarity and relevance measurement is able to improve the performance of hyperspectral image classification.

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Acknowledgments

This work is jointly supported by the International Science and Technology Collaboration Project of China (2010DFA92720-24), National Natural Science Foundation program (No. 41301403 and No. 41471340); Chongqing Basic and Advanced Research General Project (No. cstc2013jcyjA40010); Hunan Provincial Natural Science Foundation of China (No. S2013J504B). The authors of this paper would also like to appreciate Prof. Paolo Gamba for his kindly providing hyperspectral image data of Pavia University, Pavia, northern Italy.

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Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China

    Jun Liu

  2. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China

    Shuguang Liu

  3. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, 85287-5302, USA

    Xiran Zhou

  4. Department of Geography, Hong Kong Baptist University, Hong Kong, China

    Junyi Huang

  5. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan, China

    Huali Li

  6. Yunnan Electronic Computing Center, Kunming, 650223, Yunnan, China

    Shan Wen

  7. Tianjin Institute of Surveying and Mapping, Tianjin, 300381, China

    Junchen Liu

Authors
  1. Jun Liu
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  2. Xiran Zhou
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  3. Junyi Huang
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  4. Shuguang Liu
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  5. Huali Li
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  6. Shan Wen
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  7. Junchen Liu
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Correspondence to Shuguang Liu.

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Liu, J., Zhou, X., Huang, J. et al. Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine. Multimedia Systems 23, 95–104 (2017). https://doi.org/10.1007/s00530-015-0455-8

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  • DOI: https://doi.org/10.1007/s00530-015-0455-8

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