Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2018
Abstract
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.
Keywords
Unsupervised anomaly detection, Anomaly segmentation, Self-supervised learning
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 29th British Machine Vision Conference 2018: Norththumbria, September 3-6
First Page
1
Last Page
13
Publisher
BMVA Press
City or Country
Newcastle
Citation
SINGH, Suriya; BATRA, Anil; PANG, Guansong; TORRESANI, Lorenzo; BASU, Saikat; PALURI, Manohar; and JAWAHAR, C. V..
Self-supervised feature learning for semantic segmentation of overhead imagery. (2018). Proceedings of the 29th British Machine Vision Conference 2018: Norththumbria, September 3-6. 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/8141
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.