A Self-Training Approach Using Benchmark Dataset and Stereo-DSM for Building Extraction

Abstract

Deep learning has been the state-of-the-art solution to numerous remote sensing tasks, especially for building extraction. However, the performance of learning-based building extraction approaches depend to a large extent on the similarity of the source and target domain data. To alleviate the dependence on annotated data, and to exploit the potential of multimodal remote sensing data, a 3-D assisted semisupervised method for building extraction is proposed. The proposed method is based on self-training, a semisupervised method that utilizes both labeled and unlabeled data. In addition, photogrammetric digital surface model and belief function are exploited to bridge the domain gaps between the source and target data. The performance is evaluated with ISPRS Potsdam and Vaihingen benchmark datasets, and a WorldView-2 satellite multimodal dataset. Compared with the direct cross-domain test baseline, improvement of Jaccard score ranging from 8.91% to 21.39% is achieved, demonstrating the efficacy of the proposed 3-D self-training method.

Keywords

• Aerial imagery
• DSM
• deep learning
• domain gap
• multimodal
• pseudolabeling