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.

中文翻译：

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SSGAN：使用时空光谱生成对抗网络去除卫星图像中的云


卫星数据的可靠性、一致性和全球扫描功能彻底改变了农业监测和作物管理。然而，卫星图像中云的存在会掩盖有用的信息，使其难以推断。针对云覆盖问题，该文提出了一种从多光谱卫星图像中有效消除云覆盖的时空光谱生成对抗网络 （SSGAN） 方法。它利用合成孔径雷达 （SAR） 图像作为来自 Sentinel-2 卫星的光学图像的补充信息。所提出的模型通过根据电磁波长对 Sentinel-2 图像的 13 个通道进行子分组来利用特征提取。通过实验，我们证明所提出的 SSGAN 模型超越了传统和最先进的 （SOTA） 方法，并且可以重建被云遮挡的区域。子分组优化了传感器信息的利用率，并提高了重建图像的性能指标。与最先进的 （SOTA） 方法相比，SSGAN 模型表现出更高的性能，实现了 32.771 的 mPSNR、0.880 的 mSSIM 和0.889 的相关系数 （CC）。SSGAN 模型在不同条件下进行了进一步评估，包括不包含 SAR 数据的情况，其中它实现了 26.825 的 mPSNR、0.726 的 mSSIM 和 0.615 的 CC。将 SAR 图像添加到模型中可显著提高其性能，从而得到 mPSNR 为 29.932、mSSIM 为 0.857 和 CC 为 0.735。这些结果表明，较高的 mPSNR 、 mSSIM 和 CC 值对应于更好的图像重建质量。我们的方法增强了卫星数据在作物测绘、作物健康监测和作物产量预测方面的可用性。