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...