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E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network

Yuting Wan, Yanfei Zhong, Ailong Ma

    IEEE Transactions on Neural Networks and Learning Systems
    |November 17, 2022
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    Summary
    This summary is machine-generated.

    This study introduces E2SCNet, an efficient framework for automatically designing deep learning networks for remote sensing image scene classification. It balances accuracy and model size, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Remote Sensing

    Background:

    • Deep learning network architectures for remote sensing image scene classification often rely on fixed, natural image processing methods.
    • Existing automatic search mechanisms struggle to balance interpretation accuracy and parameter quantity for practical applications.
    • Multiobjective evolutionary computation offers advantages for automatic global search but faces challenges in retaining high-accuracy, large-parameter models.

    Purpose of the Study:

    • To propose an efficient multiobjective evolutionary automatic search framework, E2SCNet, for deep learning network architectures in remote sensing image scene classification.
    • To address concerns regarding the elimination of large-parameter networks and the lengthy search times of current evolutionary methods.
    • To develop a method that effectively balances accuracy and parameter quantity for practical remote sensing applications.

    Main Methods:

    • E2SCNet utilizes eight lightweight operators to create a flexible and diversified search space.
    • A two-step multiobjective modeling and evolutionary search process is employed, considering "parameter quantity and accuracy" and "parameter quantity and accuracy growth quantity" for model retention.
    • A super network is constructed for weight sharing during individual network evaluation to accelerate the search process.

    Main Results:

    • E2SCNet demonstrates effectiveness in designing deep learning architectures for remote sensing image scene classification.
    • The proposed framework successfully balances model accuracy and parameter quantity, addressing limitations of previous methods.
    • The search speed is promoted through weight sharing in a constructed super network.

    Conclusions:

    • E2SCNet provides an efficient and effective solution for automatically searching deep learning network architectures for remote sensing image scene classification.
    • The framework's ability to retain large models and its accelerated search speed make it suitable for practical applications.
    • E2SCNet outperforms human-designed networks and networks found through other search methods.