Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This article presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net [1]) that is trained with a novel loss function [filtered Jaccard loss (FJL)]. The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, FJL function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications.

中文翻译：

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通过滤波 Jaccard 损失函数和参数增强进行遥感图像的云和云阴影分割


云和云阴影分割是光学遥感图像分析的基本过程。当前地理空间图像中云/阴影识别的方法并不像应有的那么准确，特别是在存在雪和雾的情况下。本文提出了一种基于深度学习的框架，用于检测 Landsat 8 图像中的云/阴影。我们的方法受益于卷积神经网络 Cloud-Net+（我们之前提出的 Cloud-Net [1] 的修改版），该网络使用新颖的损失函数 [过滤 Jaccard 损失 (FJL)] 进行训练。所提出的损失函数对图像中前景物体的缺失更敏感，并且比其他常见损失函数更准确地惩罚/奖励预测掩模。此外，针对云阴影检测任务开发了一种阳光方向感知数据增强技术，通过扩展现有的训练集来扩展所提出模型的泛化能力。 Cloud-Net+、FJL 函数和所提出的增强算法的组合在四个公共云/阴影检测数据集上提供了出色的结果。我们在 Pascal VOC 数据集上的实验证明了我们提出的网络和损失函数在其他计算机视觉应用中的适用性和质量。