Mitigating Cloud Contamination for Accurate Data Retrieval in Remote Sensing Applications
DOI:
https://doi.org/10.54228/mjaret0624013Keywords:
Cloud Contamination; Remote Sensing; Data Retrieval; Image Reconstruction; Denoising Diffusion Probabilistic Models; Spatiotemporal Fusion; Sentinel-2; Environmental MonitoringAbstract
In this study, we propose a new hybrid approach to remove cloud contamination from remote sensing images for more accurate data extraction. It involves using a novel Denoising Diffusion Probabilistic Feature-Based Network (DDPFN) together with a spatiotemporal fusion approach for cloud removal and image reconstruction. The method was tested on a large dataset of Landsat-8 and Sentinel-2 imagery with different land cover types and cloud conditions. The results show a 35% improvement in the cloud mask accuracy and a 40% reduction in reconstruction error compared to baseline methods. Our proposed method can effectively remove both thin and thick clouds, and maintain high fidelity in underlying surface information for more reliable and continuous data acquisition. The outcome of this research can facilitate better remote sensing for environmental monitoring, land cover classification and climate change studies in cloud-prone regions.
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