  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.
