ABSTRACT Clouds lead to missing or distorted land-related information in impacted areas in optical remote sensing images. Cloud masking, which labels cloud-contaminated pixels, forms the basis for subsequent image utilization, such as excluding the distorted pixel or filling in the missing area. However, due to the diverse spectral, textural, and shape characteristics of different clouds and complicated combinations with the underlying land surfaces, cloud masking has become a challenge in remote sensing image processing. In recent years, the Mask region-based convolutional neural network (R-CNN) method, which performs instance segmentation from a complex background and generates a pixelwise mask for the object of interest, has been used widely in object segmentation tasks. When the Mask R-CNN method is used for cloud masking, the mask result has certain problems, such as failing to extract uncommon clouds and outputting inaccurate mask boundaries for large clouds. To address these problems, we introduce two strategies, group training and boundary optimization, to improve the Mask R-CNN. For group training, samples are divided into several groups. The samples in the first group are used for the initial training, and the samples in the next group are used for evaluation. Only samples with missing or falsely detected clouds are used for tuning the classifier; then, these processes are repeated until all groups have been used or the detection precision becomes stable. For boundary optimization, a block-by-block mask strategy is adopted to guarantee that clouds with diverse sizes have similar performances. Finally, two open data sets and one data set labelled by ourselves are selected to test the proposed method, and the results demonstrate that our method can produce cloud masks for different cloud types and diverse underlying land surfaces and can achieve high accuracies, thereby providing an effective alternative for cloud masking. Compared with the original Mask R-CNN method, our method improves the average recall, average precision, and intersection over union by 5.88%, 2.4%, and 0.071 in pixel level, respectively, demonstrating the effectiveness of our improvement.

中文翻译：

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改进的基于mask R-CNN的遥感图像云掩蔽方法

摘要 云会导致光学遥感图像中受影响地区的土地相关信息丢失或失真。云遮蔽，标记受云污染的像素，构成后续图像利用的基础，例如排除失真像素或填充缺失区域。然而，由于不同云的光谱、纹理和形状特征的多样性以及与下垫面的复杂组合，云掩蔽已成为遥感图像处理中的一个挑战。近年来，基于掩码区域的卷积神经网络 (R-CNN) 方法从复杂背景中执行实例分割并为感兴趣的对象生成像素掩码，已广泛用于对象分割任务。当Mask R-CNN方法用于云掩蔽时，mask结果存在一定的问题，如无法提取不常见的云，大云输出的mask边界不准确。为了解决这些问题，我们引入了两种策略，组训练和边界优化，以改进 Mask R-CNN。对于组训练，样本被分成几组。第一组样本用于初始训练，下一组样本用于评估。仅使用丢失或错误检测到云的样本来调整分类器；然后，重复这些过程，直到所有组都被使用或检测精度变得稳定。对于边界优化，采用逐块掩码策略来保证不同大小的云具有相似的性能。最后，选择两个开放数据集和一个我们自己标记的数据集来测试所提出的方法，结果表明我们的方法可以为不同的云类型和不同的下垫面生成云掩码，并且可以达到较高的精度，从而提供了一种有效的替代方法用于云遮蔽。与原始的 Mask R-CNN 方法相比，我们的方法在像素级分别将平均召回率、平均精度和交集提高了 5.88%、2.4% 和 0.071，证明了我们改进的有效性。