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Select, Purify, and Exchange: A Multisource Unsupervised Domain Adaptation Method for Building Extraction.

Shuang Wang, Qi Zang, Dong Zhao

    IEEE Transactions on Neural Networks and Learning Systems
    |July 13, 2023
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    Summary

    This study introduces SPENet, a multisource unsupervised domain adaptation framework for building extraction from aerial images. It improves model generalization by effectively selecting, purifying, and exchanging information across multiple data sources.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Machine Learning

    Background:

    • Accurate building extraction from aerial imagery is crucial for land use analysis.
    • Deep learning models struggle with domain shift in remote sensing data (sensor, location, environment).
    • Unsupervised domain adaptation (UDA) addresses this by adapting models without re-annotation, but single-source UDA (SSUDA) has limitations with diverse data.

    Purpose of the Study:

    • To develop a novel multisource unsupervised domain adaptation (MSUDA) framework, SPENet, for enhanced building extraction.
    • To overcome the limitations of SSUDA by leveraging information from multiple source domains.
    • To improve the generalization performance of deep learning models on diverse remote sensing datasets.

    Main Methods:

    • SPENet framework for building extraction using multisource UDA.
    • Selection, purification, and interactive exchange of information from multiple source domains.
    • Utilizing target-relevant information and purifying target domain data with low-level building features.

    Main Results:

    • SPENet demonstrates superior performance on building extraction tasks across 12 city datasets.
    • Achieved 59.1% intersection over union (IoU) on Austin and Kitsap → Potsdam.
    • Outperformed the target domain supervised method by 2.2% on specific datasets, validating its effectiveness.

    Conclusions:

    • The proposed SPENet framework effectively addresses domain discrepancies in remote sensing data for building extraction.
    • MSUDA, by integrating information from multiple sources, significantly enhances model adaptability and performance.
    • SPENet offers a robust solution for building extraction, outperforming existing state-of-the-art methods.