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.

本研究旨在填补深度学习在基于对象的图像分析（OBIA）中的应用空白，探讨其整合中的挑战和策略。文章综合回顾了OBIA的任务子领域，并提出了应对深度学习处理非结构化对象数据限制的五种主要策略，旨在启发对这一被忽视领域的进一步研究。

深度学习与基于对象的图像分析：任务、挑战、策略与展望