Date of Award
Master of Science (MS)
Chaity Banerjee Mukherjee
Remote sensing, Image segmentation, Deep learning (Machine learning)
Image segmentation is typically done through supervised learning. Supervised learning requires labeled data, which is costly and time-consuming to acquire. However, unlabeled data is abundant. This research presents an application of the state-of-the-art unsupervised instance segmentation method FreeSOLO in satellite images and benchmarks the method in iSAID, CrowdAI, and PASTIS datasets. The method achieved 0.9%AP50 in the iSAID dataset, 3.1%AP50 on the CrowdAI dataset, and 1.1%AP50 on the PASTIS dataset. On large objects, it achieved 1.2%AP50 in the iSAID dataset and 3.5%AP50 in the CrowdAI dataset. The method was also tested in the UAH periphery and MBRSC Dubai dataset where the model was able to segment buildings, water bodies, highways, apartments, and trees. This research also demonstrates the comparative performance of FreeSOLO-based weights relative to other popular supervised learning-based encoder weights on semantic segmentation downstream task.
Regmi, Suraj, "Unsupervised image segmentation in satellite imagery using deep learning" (2023). Theses. 458.