Deep Learning Meets Object-Based Image Analysis Tasks, challenges, strategies, and perspectives
Document Type
Article
Publication Date
11-27-2024
Department
College of Forest Resources and Environmental Science
Abstract
Deep remote learning sensing, (DL) especially has gained in pixel- significant or patch-level attention appli- in cations. Despite initial attempts to integrate DL into object-based image analysis (OBIA), its full potential remains largely unexplored. In this article, as OBIA usage becomes more widespread, we conduct a comprehensive review and expansion of its task subdomains, with or without the integration of DL. Furthermore, we identify and summarize five prevailing strategies to address the challenge of DL’s limitations in directly processing unstructured object data within OBIA, and this review also recommends some important future research directions. Our goal with these endeavors is to inspire more exploration in this fascinating yet overlooked area and facilitate the integration of DL into OBIA processing workflows.
Publication Title
IEEE Geoscience and Remote Sensing Magazine
Recommended Citation
Ma, L.,
Yan, Z.,
Li, M.,
Liu, T.,
Tan, L.,
Wang, X.,
He, W.,
Wang, R.,
He, G.,
Lu, H.,
&
Blaschke, T.
(2024).
Deep Learning Meets Object-Based Image Analysis Tasks, challenges, strategies, and perspectives.
IEEE Geoscience and Remote Sensing Magazine.
http://doi.org/10.1109/MGRS.2024.3489952
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/1317
