ABSTRACT

Cloud detection for satellite cloud images is a challenging image processing task owing to the blurring of cloud boundaries, multiplicity, and complexity of cloud types. Currently, the commonly used cloud detection methods include original full convolutional neural network (original FCN), FCN with an 8-pixel stride (FCN- 8s), FCN with a 2-pixel stride (FCN-2s), and so on. However, the aforementioned methods exclusively rely on a single network layer $-$ the final layer feature map; thus, shallow cloud image information, such as cloud profile information may not be captured. In this letter, a cloud detection method for satellite cloud images based on fused FCN features is proposed. The proposed method effectively fuses spatial and high-level semantic information, and a voting ensemble strategy is used to improve the accuracy and robustness of cloud detection. Finally, the experimental results demonstrate that the average overall accuracy (OA), average producer’ accuracy (PA), and average user’ accuracy (UA) of the proposed method for multiple training sample sizes and image sizes of the collected Fengyun satellite (FY-2 G) cloud image database increased by 7.15%, 9.04%, and 8.46%, respectively, relative to the average accuracies of the original FCN, FCN-8s, FCN-2s, SegNet, and DeepLabV3 methods.

摘要

由于云边界的模糊、云类型的多样性和复杂性，卫星云图像的云检测是一项具有挑战性的图像处理任务。目前，常用的云检测方法包括原始全卷积神经网络（original FCN）、步长为8的FCN（FCN-8s）、步长为2的FCN（FCN-2s）等。然而，上述方法完全依赖于单个网络层$-$最后一层特征图；因此，可能无法捕获浅层云图像信息，例如云轮廓信息。本文提出了一种基于融合FCN特征的卫星云图云检测方法。该方法有效地融合了空间和高级语义信息，并采用投票集成策略来提高云检测的准确性和鲁棒性。最后，实验结果表明，该方法对采集的风云卫星（FY -2 G) 云图像数据库相对于原始 FCN、FCN-8s、FCN-2s、SegNet 和 DeepLabV3 方法的平均精度分别提高了 7.15%、9.04% 和 8.46%。