Authors - S.Nagarjuna Reddy, B.Lakshmi Priyanka, E.Vamsi, G.Raja Shekar Reddy Abstract - Cloud cover, shadows, haze, illumination variation, and atmospheric noise severely degrade the reliability of satellite image change detection. This paper proposes an atmosphere-aware, physics-driven preprocessing framework that performs cloud, shadow, haze, and illumination compensation before change analysis, without relying on convolutional or transformer-based networks. Two multi-temporal satellite images are processed through unified cloud and shadow handling, haze correction, illumination normalization, and residual atmospheric noise suppression, followed by a spectrally invariant change detector with structural consistency validation. The system also generates semantic multi-class change maps and geo-contextual text explanations to enhance interpretability. Experiments on diverse multi-temporal datasets demonstrate a change detection accuracy of 98.9% with high precision and recall, significantly outperforming conventional and deep learning baselines
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