CCAD-Net: A Cascade Cloud Attribute Discrimination Network for Cloud Genera Segmentation in Whole-Sky Images

Cloud detection and recognition are two important tasks usually referring to image binary segmentation and image-level classification individually. Cloud genera segmentation has more practical significance but is much more challenging as a fine-grained pixel-level dense prediction problem. In this letter, a cascade cloud attribute discrimination network (CCAD-Net) is proposed. Based on an improved encoding–decoding model, CCAD-Net adds a binary segmentation branch for cloud detection and an attribute discrimination branch for cloud attribute feature learning in the decoding stage. Especially, in the attribute discrimination branch, several visual attributes are selected to design the attribute discrimination constraint according to prior professional knowledge and the corresponding loss function is defined. These two additional branches and the final cloud genera segmentation branch extract their task-specific features successively and form a cascade structure. Due to the fusion of raw feature, binary segmentation feature, attribute discrimination feature, and cloud genera feature, CCAD-Net can achieve significantly better performance than the state-of-the-art methods in cloud genera segmentation in whole-sky images.