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Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives

arXiv 2408.01607 ↗First posted 2 Aug 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Deep learning has gained significant attention in remote sensing, especially in pixel- or patch-level applications. Despite initial attempts to integrate deep learning into object-based image analysis (OBIA), its full potential remains largely unexplored. In this article, as OBIA usage becomes more widespread, we conducted a comprehensive review and expansion of its task subdomains, with or without the integration of deep learning. Furthermore, we have identified and summarized five prevailing strategies to address the challenge of deep learning'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 deep learning into OBIA processing workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 36 of its 257 references that are in the map. First posted on arXiv 2 Aug 2024.

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References · 36

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24 more
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