协同推进数据驱动的深度学习和知识引导的本体推理，用于遥感图像的语义分割
Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery
摘要：
深度语义分割网络（Deep Semantic Segmentation Network，DSSN）作为深度学习家族中的一种架构，在语义分割任务上取得了一定程度的成功，明显优于传统的基于手工特征的方法。作为一种经典的数据驱动技术，DSSN可以通过端到端的机制进行训练，并且能够使用低级和中级提示（即，区别性图像结构）来理解图像，但是缺乏高级推理能力。相比之下，人类具有很好的推理能力，只有当人类掌握了基本的RS领域知识，才能可靠地解释RS图像。本体建模和推理是一种模仿和利用人类领域知识的理想方法，但在RS领域中还很少被探索和采用。为了弥补DSSN的上述关键限制，本文提出了一种协同提升框架（CBF），以迭代的方式将数据驱动的深度学习模块和知识引导的本体推理模块相结合。
英文摘要：
As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-to-end mechanism and competent for employing the low-level and mid-level cues (i.e., the discriminative image structure) to understand images, but lacks the high-level inference ability. By contrast, human beings have an excellent inference capacity and can be able to reliably interpret the RS imagery only when human beings master the basic RS domain knowledge. In literature, ontological modeling and reasoning is an ideal way to imitate and employ the domain knowledge of human beings, but is still rarely explored and adopted in the RS domain. To remedy the aforementioned critical limitation of DSSN, this paper proposes a collaboratively boosting framework (CBF) to combine data-driven deep learning module and knowledge-guided ontological reasoning module in an iterative way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
