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English(EN) Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction

新型GAN高精度重建卫星陆地表面温度数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的多模态快速傅里叶卷积GAN，用于从卫星图像重建陆地表面温度（LST）数据，特别解决了云覆盖造成的间隙问题。该方法利用快速傅里叶卷积实现全局感受野，并整合了卫星观测和合成孔径雷达（SAR）数据。该方法在恢复大片缺失区域方面表现出有效性，即使在云引起的间隙超过70%的场景中，在所有LST分位数上，场景平均RMSE的四分位距也介于0.8 K和1.8 K之间。 AI

影响 该方法可以提高基于卫星的气候和环境监测的准确性和完整性。

排序理由 这是一篇详细介绍数据重建新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新型GAN高精度重建卫星陆地表面温度数据

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Marwa Alfouly, Smajil Halilovic, Nils Bochow, Thomas Hamacher, Niklas Boers, Konrad Schindler ·

    用于 30 米晴空地表温度无缝重建的快速傅里叶卷积 GAN

    arXiv:2607.22734v1 Announce Type: new Abstract: Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data gaps due to the presence of clouds. As LST is essent…

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