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Asia-Pacific Web Conference

APWeb 2016: Web Technologies and Applications pp 179–190Cite as

Scene Classification in High Resolution Remotely Sensed Images Based on PCANet

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

Abstract

Rich information provided by high resolution remotely sensed images allow us to classify scenes by understanding their spatial and structural patterns. The key of scene classification task with remotely sensed images lies in feature learning efficiency and invariant image representations. While deep neutral network-based approaches achieved good classification accuracy for remotely sensed images, they often have to train millions of parameters and involve heavily iterative computation. In this paper, we propose a new framework for scene classification based on a simple PCANet which is introduced into high remotely sensed image classification for the first time. First, we verify the eligibility of PCANet on classifying large scale scenes from high resolution remotely sensed images. Then we explore the impact of PCANet parameters including filter size, number of filters, and block overlap ratio on classification accuracy. Lastly, we do comprehensive experiments with the public UC-Merced dataset to exemplify the effectiveness of the approach. Experimental results show that the proposed framework achieved on par with the state-of-the-art deep neutral network-based classification accuracy without training a huge amount of parameters. We demonstrate that the proposed classification framework can be highly effective in developing a classification system that can be used to automatically scan large-scale high resolution satellite imagery for classifying scenes.

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Acknowledgments

This work is supported by National Basic Research Program of China (973 Program) Grant 2012CB316206, the National Natural Science Foundation of China (NSFC) Grant 61272098, and the Capacity Development for Local College Project Grant 15590501900.

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Authors and Affiliations

  1. College of Information Technology, Shanghai Ocean University, Shanghai, China

    Dongmei Huang, Yanling Du, Qi He & Wei Song

  2. East Sea Branch of the State Oceanic Administration, Shanghai, China

    Kefu Liu

Authors
  1. Dongmei Huang
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  2. Yanling Du
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  3. Qi He
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  4. Wei Song
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  5. Kefu Liu
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Corresponding author

Correspondence to Qi He .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Huang, D., Du, Y., He, Q., Song, W., Liu, K. (2016). Scene Classification in High Resolution Remotely Sensed Images Based on PCANet. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-45835-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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