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    This study introduces a season-specific Neural Radiance Field (NeRF) for rendering novel views from satellite imagery. The method disentangles seasonal features from shadows, improving scene representation accuracy.

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    Area of Science:

    • Computer Vision
    • Remote Sensing
    • Neural Rendering

    Background:

    • Existing Neural Radiance Field (NeRF) frameworks like Shadow NeRF and Sat-NeRF incorporate solar angles for novel view synthesis from satellite images.
    • However, these methods do not explicitly account for seasonal variations in scene appearance.

    Purpose of the Study:

    • To develop a season-specific Neural Radiance Field (NeRF) capable of rendering seasonal features independently of viewing and solar angles.
    • To ensure accurate rendering of shadows while disentangling them from seasonal characteristics.

    Main Methods:

    • Introduced 'time of the year' as an additional input variable to the NeRF to learn seasonal features.
    • Modified the loss function with additional terms to prevent the network from conflating seasonal features with shadows, especially in small satellite imagery datasets.
    • Evaluated the framework on eight Areas of Interest using Maxar WorldView-3 satellite imagery.

    Main Results:

    • The proposed NeRF framework successfully renders season-specific features independently of viewing and solar angles.
    • The method accurately predicts shadows and generates height maps.
    • Ablation studies confirmed the effectiveness of the network design choices.

    Conclusions:

    • The developed season-specific NeRF effectively disentangles seasonal appearance from illumination effects like shadows in satellite imagery.
    • This advancement enables more accurate and context-aware novel view synthesis for applications utilizing satellite data.