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SSGAN : cloud removal in satellite images using spatiospectral generative adversarial network
Publikationstyp
Journal Article
Date Issued
2024-11
Sprache
English
Author(s)
Ghildiyal, Sushil
Goel, Neeraj
Kawsar, Riazuddin
Saini, Mukesh
Journal
Volume
161
Article Number
127333
Citation
European Journal of Agronomy 161: 127333 (2024)
Publisher DOI
Scopus ID
Publisher
Elsevier Science
Satellite data's reliability, uniformity, and global scanning capabilities have revolutionized agricultural monitoring and crop management. However, the presence of clouds in satellite images can obscure useful information, rendering them difficult to infer. Aiming at the problem of cloud cover, this study presents a SpatioSpectral Generative Adversarial Network (SSGAN) approach for effectively eliminating cloud cover from multispectral satellite images. It utilizes the Synthetic Aperture Radar (SAR) images as complementary information with the optical images from the Sentinel-2 satellite. The proposed model exploits feature extraction by sub-grouping the 13 channels of Sentinel-2 images based on their electromagnetic wavelength. Experimentally, we demonstrated that the proposed SSGAN model surpasses conventional and state-of-the-art (SOTA) methods and can reconstruct regions obscured by clouds. The subgrouping optimized the utilization of sensor information and improved the performance metrics for reconstructed images. Compared to the state-of-the-art (SOTA) approach, the SSGAN model demonstrates higher performance, achieving a mPSNR of 32.771, mSSIM of 0.880, and correlation coefficient (CC) of 0.889. The SSGAN model was further evaluated under varying conditions, including scenarios without the inclusion of SAR data, where it achieved a mPSNR of 26.825, mSSIM of 0.726, and CC of 0.615. Adding SAR images into the model significantly enhanced its performance, resulting in a mPSNR of 29.932, mSSIM of 0.857, and CC of 0.735. These results indicate that higher mPSNR, mSSIM, and CC values correspond to better image reconstruction quality. Our method enhances the usability of satellite data for crop mapping, crop health monitoring, and crop yield prediction.
Subjects
Cloud removal
Crop health monitoring
Crop management
Deep learning
Generative adversarial network
Optical-SAR
Remote sensing
Satellite images
DDC Class
004: Computer Sciences
006: Special computer methods
630: Agriculture and Related Technologies