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Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

This paper presents a survey of evolutionary computation theory for remote sensing image clustering. With the ongoing development of Earth observation techniques, remote sensing data has entered the era of big data, so it is difficult for researchers to get more prior knowledge. In recent years, many experts and scholars have a strong interest in remote sensing clustering due to it does not require training samples. However, remote sensing image clustering has always been a challenging task because of the inherent complexity of remote sensing images, the huge amount of data and so on. Normally, the clustering problem of remote sensing images is transformed into the optimization problem of fuzzy clustering objective function, the goal of which lies in the identification of correct cluster centers in the eigenspace. But traditional clustering approaches belong to hill climbing methods, which are greatly affected by initial values and easily get stuck in local optima. Evolutionary computation techniques are inspired by biological evolution, which can provide possible solutions to find the better clustering centers. So, researchers have carried out a series of related studies. Here, we provide an overview, including: (1) evolutionary single-objective; (2) evolutionary multi-objective; (3) memetic algorithm.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430000, China

    Yuting Wan, Yanfei Zhong, Ailong Ma & Liangpei Zhang

Authors
  1. Yuting Wan
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  2. Yanfei Zhong
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  3. Ailong Ma
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  4. Liangpei Zhang
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Corresponding author

Correspondence to Ailong Ma .

Editor information

Editors and Affiliations

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

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

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

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Wan, Y., Zhong, Y., Ma, A., Zhang, L. (2017). Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_43

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_43

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-68759-9

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