SSGAN:利用空间光谱生成对抗网络去除卫星图像中的云层

IF 4.5 1区 农林科学 Q1 AGRONOMY European Journal of Agronomy Pub Date : 2024-09-13 DOI:10.1016/j.eja.2024.127333
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引用次数: 0

摘要

卫星数据的可靠性、统一性和全球扫描能力为农业监测和作物管理带来了革命性的变化。然而,卫星图像中云层的存在会掩盖有用信息,使其难以推断。针对云层问题,本研究提出了一种空间光谱生成对抗网络(SSGAN)方法,可有效消除多光谱卫星图像中的云层。它利用合成孔径雷达(SAR)图像作为哨兵-2 卫星光学图像的补充信息。所提出的模型通过对哨兵-2 号卫星图像的 13 个信道进行基于电磁波长的分组来提取特征。实验证明,所提出的 SSGAN 模型超越了传统和最先进的(SOTA)方法,可以重建被云层遮挡的区域。分组优化了传感器信息的利用,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,mPSNR 达到 32.771,mSSIM 达到 0.880,相关系数(CC)达到 0.889。SSGAN 模型在不同条件下进行了进一步评估,包括未包含合成孔径雷达数据的情况,其 mPSNR 为 26.825,mSSIM 为 0.726,CC 为 0.615。在模型中加入合成孔径雷达图像后,其性能显著提高,mPSNR 为 29.932,mSSIM 为 0.857,CC 为 0.735。这些结果表明,更高的 mPSNR、mSSIM 和 CC 值对应着更好的图像重建质量。我们的方法提高了卫星数据在作物绘图、作物健康监测和作物产量预测方面的可用性。
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SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network

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.

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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
期刊介绍: The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics: crop physiology crop production and management including irrigation, fertilization and soil management agroclimatology and modelling plant-soil relationships crop quality and post-harvest physiology farming and cropping systems agroecosystems and the environment crop-weed interactions and management organic farming horticultural crops papers from the European Society for Agronomy bi-annual meetings In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.
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