Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
This work addresses cloud detection for remote sensing applications, offering incremental improvements over existing methods.
This paper tackles the problem of accurate cloud detection in remote sensing images by proposing a deep-learning framework using a Fully Convolutional Neural Network and a gradient-based method to exclude snow/ice regions, resulting in average improvements of 4.36% in Jaccard index and 3.62% in recall.
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.