A Deep Architecture Based on a Two-Stage Learning for Semantic Segmentation of Large-Size Remote Sensing Images

Remote sensing images (RSIs) usually have much larger size compared to typical natural images used in computer vision applications. This makes the computational cost of training convolutional neural networks with full-size images unaffordable. Commonly used methodologies for semantic segmentation of RSIs perform training and prediction on cropped local image patches. Thus they fail to model the potential dependencies between ground objects at a higher level of abstraction. In order to better exploit global context information in RSIs, a deep architecture based on a two-stage training approach that is specially tailored to training large-size RSIs is proposed. In the first training stage, down-scaled images are used as input to learn high-level features from a large image area. In the second training stage, a local feature extraction network is designed to extract low-level information from cropped image patches. The complementary information learned from different levels is fused to make the prediction. As a result, the proposed two-stage training approach is able to exploit the context information of RSIs from a larger perspective without losing spatial details. Experimental results on a benchmark remote sensing dataset demonstrate the effectiveness of the proposed approach.