The long-term existence and extensive coverage of thick clouds have caused an amount of missing information in hyperspectral images (HSIs), hindering their applications greatly. Recently, with the continuous launch of new satellites, the increasing availability of HSIs provides more opportunities for using temporal information in cloud removal of HSIs. In this paper, a fast spatial-spectral random forests (FSSRF) method was proposed for removing clouds of HSIs. FSSRF is developed based on the advanced spatial-spectral random forests (SSRF) method that was developed to handle multispectral images. FSSRF greatly improves the computing efficiency while ensuring the accuracy of reconstruction. Experimental results on both GF-5 and EO-1 HSIs show that FSSRF is a much faster version than the original SSRF that uses directly all known bands of the temporally neighboring HSI with spatially complete coverage, and the accuracies of the two versions are similar. Compared with subSSRF (i.e., SSRF using several spectrally adjacent known bands for each cloudy band) and the popular MNSPI method, FSSRF can produce more accurate predictions. The evaluation from spectral dimension shows that FSSRF can recover the spectral characteristics of cloud pixels more satisfactorily. FSSRF has great potential in real-time cloud removal of HSIs due to its obvious advantages in balancing computational efficiency and accuracy.

厚云的长期存在和广泛覆盖导致高光谱图像（HSI）中的大量信息缺失，极大地阻碍了其应用。近来，随着新卫星的不断发射，HSI 的可用性越来越高，这为在 HSI 的云清除中使用时间信息提供了更多机会。在本文中，提出了一种快速空间光谱随机森林（FSSRF）方法来去除HSI的云。FSSRF 是基于用于处理多光谱图像的高级空间光谱随机森林 (SSRF) 方法开发的。FSSRF在保证重构精度的同时大大提高了计算效率。GF-5 和 EO-1 HSI 的实验结果表明，FSSRF 是一个比原始 SSRF 快得多的版本，它直接使用时间相邻 HSI 的所有已知波段，具有空间完整覆盖，并且两个版本的精度相似。与 subSSRF（即 SSRF 对每个多云波段使用几个光谱相邻的已知波段）和流行的 MNSPI 方法相比，FSSRF 可以产生更准确的预测。从光谱维度上的评价表明，FSSRF能够较满意地恢复云像素的光谱特征。FSSRF由于在平衡计算效率和准确性方面的明显优势，在HSI的实时去云方面具有巨大的潜力。SSRF 对每个多云波段使用几个光谱相邻的已知波段）和流行的 MNSPI 方法，FSSRF 可以产生更准确的预测。从光谱维度上的评价表明，FSSRF能够较满意地恢复云像素的光谱特征。FSSRF由于在平衡计算效率和准确性方面的明显优势，在HSI的实时去云方面具有巨大的潜力。SSRF 对每个多云波段使用几个光谱相邻的已知波段）和流行的 MNSPI 方法，FSSRF 可以产生更准确的预测。从光谱维度上的评价表明，FSSRF能够较满意地恢复云像素的光谱特征。FSSRF由于在平衡计算效率和准确性方面的明显优势，在HSI的实时去云方面具有巨大的潜力。