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High Spatial Resolution Remote Sensing Imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

In remote sensing image classification, it is difficult to distinguish the homogeneity of same land class and the heterogeneity between different land classes. Moreover, high spatial resolution remote sensing images often show the phenomenon of ground object classes fragmentation and salt-and-pepper noise after classification. To improve the above phenomenon, Markov random field (MRF) is a widely used method for remote sensing image classification due to its effective spatial context description. Some MRF-based methods capture more image information by building interaction between pixel granularity and object granularity. Some other MRF-based methods construct representations at different semantic layers on the image to extract the spatial relationship of objects. This paper proposes a new MRF-based method that combines multi-granularity and different semantic layers of information to improve remote sensing image classification. A hierarchical interaction algorithm is proposed that iteratively updates information between different granularity and semantic layers to generate results. The experimental results demonstrate that: on the Gaofen-2 imagery, the proposed model shows a better classification performance than other methods.

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Author information

Authors and Affiliations

  1. Faculty of Forestry, Southwest Forestry University, Kunming, China

    Jun Wang, Haoyu Fu & Yue Zhang

  2. Faculty of Art and Design, Southwest Forestry University, Kunming, China

    Qinling Dai

  3. Faculty of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, China

    Leiguang Wang & Yili Zhao

Authors
  1. Jun Wang
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  2. Qinling Dai
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  3. Leiguang Wang
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  4. Yili Zhao
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  5. Haoyu Fu
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  6. Yue Zhang
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Corresponding author

Correspondence to Qinling Dai .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

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

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

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

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Wang, J., Dai, Q., Wang, L., Zhao, Y., Fu, H., Zhang, Y. (2022). High Spatial Resolution Remote Sensing Imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_39

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

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