Self-supervised feature learning for semantic segmentation of overhead imagery

• Main Authors: SINGH, Suriya, BATRA, Anil, PANG, Guansong, TORRESANI, Lorenzo, BASU, Saikat, PALURI, Manohar, JAWAHAR, C. V.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Unsupervised anomaly detection; Anomaly segmentation; Self-supervised learning; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8141; https://ink.library.smu.edu.sg/context/sis_research/article/9144/viewcontent/Semi_supervised_0345_BVC_2018_pvoa_cc_by.pdf
• Institution: Singapore Management University

Overhead imageries play a crucial role in many applications such as urban planning, crop yield forecasting, mapping, and policy making. Semantic segmentation could enable automatic, efficient, and large-scale understanding of overhead imageries for these applications. However, semantic segmentation of overhead imageries is a challenging task, primarily due to the large domain gap from existing research in ground imageries, unavailability of large-scale dataset with pixel-level annotations, and inherent complexity in the task. Readily available vast amount of unlabeled overhead imageries share more common structures and patterns compared to the ground imageries, therefore, its large-scale analysis could benefit from unsupervised feature learning techniques. In this work, we study various self-supervised feature learning techniques for semantic segmentation of overhead imageries. We choose image semantic inpainting as a self-supervised task [36] for our experiments due to its proximity to the semantic segmentation task. We (i) show that existing approaches are inefficient for semantic segmentation, (ii) propose architectural changes towards self-supervised learning for semantic segmentation, (iii) propose an adversarial training scheme for self-supervised learning by increasing the pretext task’s difficulty gradually and show that it leads to learning better features, and (iv) propose a unified approach for overhead scene parsing, road network extraction, and land cover estimation. Our approach improves over training from scratch by more than 10% and ImageNet pre-trained network by more than 5% mIOU.