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Learning to Holistically Detect Bridges From Large-Size VHR Remote Sensing Imagery.

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    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    This study introduces GLH-Bridge, a large dataset for detecting bridges in remote sensing images, and HBD-Net, an efficient network for accurate detection in large, very-high-resolution images.

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    Area of Science:

    • Computer Vision
    • Remote Sensing
    • Machine Learning

    Background:

    • Bridge detection in remote sensing images (RSIs) is challenging due to variations in scale and aspect ratio.
    • Existing deep learning methods struggle with large-size VHR RSIs due to GPU memory limitations and cropping strategies causing fragmented predictions.
    • A scarcity of large-scale datasets hinders the performance of deep learning algorithms for bridge detection in RSIs.

    Purpose of the Study:

    • To address the limitations in bridge detection within large-size very-high-resolution (VHR) remote sensing images (RSIs).
    • To introduce a comprehensive dataset and an efficient network for holistic bridge detection.
    • To establish a benchmark for evaluating bridge detection algorithms on large-scale RSIs.

    Main Methods:

    • A large-scale dataset, GLH-Bridge, comprising 6,000 VHR RSIs (2,048x2,048 to 16,384x16,384 pixels) with 59,737 manually annotated bridges (oriented and horizontal bounding boxes).
    • An efficient network for holistic bridge detection (HBD-Net) featuring a separate detector-based feature fusion (SDFF) architecture and a shape-sensitive sample re-weighting (SSRW) strategy.
    • SDFF utilizes inter-layer feature fusion (IFF) within a dynamic image pyramid (DIP) for multi-scale context, while SSRW balances regression weights for diverse aspect ratios.

    Main Results:

    • The proposed GLH-Bridge dataset facilitates robust bridge detection in large-size VHR RSIs.
    • HBD-Net demonstrates effectiveness in holistic bridge detection, outperforming existing methods on the new benchmark.
    • Cross-dataset generalization experiments confirm the strong applicability of the GLH-Bridge dataset.

    Conclusions:

    • The GLH-Bridge dataset and HBD-Net significantly advance the field of bridge detection in large-scale remote sensing imagery.
    • The developed methods overcome challenges related to image size, aspect ratio variations, and data scarcity.
    • The findings provide a valuable resource and benchmark for future research in remote sensing object detection.