Abstract
Clouds significantly affect the quality of optical satellite images, which seriously limits their precise application. Recently, deep learning has been widely applied to cloud detection and has achieved satisfactory results. However, the lack of distinctive features in thin clouds and the low quality of training samples limit the cloud detection accuracy of deep learning methods, leaving space for further improvements. In this paper, we propose a weakly supervised cloud detection method that combines spectral features and multi-scale scene-level deep network (SpecMCD) to obtain highly accurate pixel-level cloud masks. The method first utilizes a progressive training framework with a multi-scale scene-level dataset to train the multi-scale scene-level cloud detection network. Pixel-level cloud probability maps are then obtained by combining the multi-scale probability maps and cloud thickness map based on the characteristics of clouds in dense cloud coverage and large cloud-area coverage images. Finally, adaptive thresholds are generated based on the differentiated regions of the scene-level cloud masks at different scales and combined with distance-weighted optimization to obtain binary cloud masks. Two datasets, WDCD and GF1MS-WHU, comprising a total of 60 Gaofen-1 multispectral (GF1-MS) images, were used to verify the effectiveness of the proposed method. Compared to the other weakly supervised cloud detection methods such as WDCD and WSFNet, the F1-score of the proposed SpecMCD method shows an improvement of over 7.82%, highlighting the superiority and potential of the SpecMCD method for cloud detection under different cloud coverage conditions.
Abstract (translated)
云对光学卫星图像的质量有显著影响，这严重限制了它们的精确应用。近年来，深度学习在云检测方面得到了广泛应用，并取得了令人满意的结果。然而，薄云缺乏独特特征以及训练样本质量低的问题，仍然制约着深度学习方法在提高云检测准确性方面的潜力。 本文提出了一种结合光谱特性和多尺度场景级深层网络（SpecMCD）的弱监督云检测方法，以获取高度准确的像素级云掩膜。该方法首先使用一个包含多尺度场景数据集的渐进式训练框架来训练多尺度场景级云检测网络。随后通过组合多尺度概率图和基于密集云覆盖及大范围云区图像特征的云厚度图，得到像素级云概率图。最后根据不同尺度下的场景级云掩膜区域差异性生成自适应阈值，并结合距离加权优化获取二进制云掩膜。 为了验证所提出方法的有效性，我们使用了包含总计60张高分1号多光谱（GF1-MS）图像的两个数据集WDCD和GF1MS-WHU。与现有的其他弱监督云检测方法如WDCD和WSFNet相比，SpecMCD方法的F1分数提升了超过7.82%，这突显了SpecMCD方法在不同云覆盖条件下的优越性和潜力。
URL
https://arxiv.org/abs/2510.00654
