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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Sparse Tensor Filtering Network for Synthetic Aperture Radar Images Classification.

Shuyuan Yang, Min Wang, Zhixi Feng

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a Deep Sparse Tensor Filtering Network (DSTFN) for classifying synthetic aperture radar (SAR) images. The DSTFN efficiently extracts discriminative features from high-resolution SAR data, improving classification accuracy.

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    Area of Science:

    • Computer Science
    • Remote Sensing
    • Signal Processing

    Background:

    • Synthetic Aperture Radar (SAR) image recognition is challenging due to high resolution and sensitivity to target aspect.
    • Existing deep neural networks can be difficult to implement practically for SAR image analysis.

    Purpose of the Study:

    • To propose a simple and efficient Deep Sparse Tensor Filtering Network (DSTFN) for SAR image classification.
    • To address the challenges of feature extraction in high-resolution SAR data.

    Main Methods:

    • Organizing SAR images into data tensors using overlapped partitioning.
    • Developing dimension-inseparable geometric filters from a least squares support vector machine.
    • Applying learned sparse filtering of tensors and cascading filters into a deep network.

    Main Results:

    • The DSTFN effectively extracts discriminative features from SAR images.
    • Simulations demonstrated the effectiveness of the proposed DSTFN for accurate SAR image classification.

    Conclusions:

    • The DSTFN offers a viable and efficient solution for SAR image classification.
    • This method enhances feature extraction capabilities for complex SAR datasets.