论文标题
通过过滤的Jaccard损耗功能和参数增强，用于遥感图像的云和云阴影细分
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
论文作者
论文摘要
云阴影细分是光学遥感图像分析中的基本过程。地理空间图像中云/阴影识别的当前方法并不像应有的那样准确，尤其是在雪和雾兹的存在下。本文提出了一个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，云网络+（对我们先前提出的云网络\ cite {myigarss}的修改），该{myigarss}）经过新颖的损失函数（过滤后的jaccard损失）。所提出的损耗函数比图像中没有前景对象更敏感，并且比其他常见的损失函数更准确地惩罚/奖励预测的掩码。此外，为云阴影检测的任务开发了阳光方向感知的数据增强技术，以通过扩展现有的训练集来扩展所提出的模型的概括能力。云网络+，过滤的jaccard损耗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卓越的结果。我们在Pascal VOC数据集上的实验例证了我们在其他计算机视觉应用中提出的网络和损失功能的适用性和质量。
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 paper 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 \cite{myigarss}) that is trained with a novel loss function (Filtered Jaccard Loss). 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+, Filtered Jaccard Loss 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.
