Deep Encoder-Decoder Structure for Cloud Image Segmentation

Deep learning makes remarkable progress in the application of remote sensing image processing, particularly in the cloud image segmentation field. The encoder-decoder structure in deep learning is widely employed for cloud image segmentation tasks. The encoder extracts high-level semantic features from the input cloud image, while the decoder restores the semantic features to generate pixel-level segmentation results. Furthermore, skip connections are adopted to connect the encoder and the decoder. In this paper, we introduce and evaluate the representative encoder-decoder struture methods for cloud image segmentation. We focus on the design of encoder, decoder and skip connections. We conduct comparative experiments on cloud image datasets and analyze the encoder-decoder structure with different layers.