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Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly

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    This study introduces a unified deep learning framework for remote sensing change detection. It enables end-to-end unsupervised, weakly supervised, and fully supervised change detection, improving upon traditional methods.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Deep learning for change detection is a key area in remote sensing.
    • Existing unsupervised methods often rely on traditional pre-detection techniques.
    • Most end-to-end networks are designed for supervised change detection.

    Purpose of the Study:

    • To propose a unified, end-to-end framework for various change detection tasks using deep learning.
    • To integrate unsupervised, weakly supervised, regional supervised, and fully supervised change detection within a single architecture.
    • To advance unsupervised change detection by eliminating the need for traditional pre-detection methods.

    Main Methods:

    • A fully convolutional change detection framework utilizing a generative adversarial network (GAN).
    • Incorporation of a Unet segmentor for generating change detection maps.
    • An image-to-image generator to model spectral and spatial variations between multi-temporal images.
    • A discriminator to model semantic changes for supervised tasks.

    Main Results:

    • The proposed framework effectively handles unsupervised, weakly supervised, and regional supervised change detection tasks.
    • Iterative optimization of the segmentor and generator creates an end-to-end unsupervised network.
    • Adversarial training between the segmentor and discriminator addresses weakly and regional supervised tasks.
    • The segmentor alone can be trained for fully supervised change detection.

    Conclusions:

    • The developed framework offers a versatile solution for diverse remote sensing change detection scenarios.
    • It establishes new theoretical definitions for unsupervised, weakly supervised, and regional supervised change detection.
    • The framework demonstrates significant potential for advancing end-to-end deep learning in remote sensing change detection.