论文标题
协作促进数据驱动的深度学习和知识引导的本体论推理，以进行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
论文作者
论文摘要
作为深度学习家族的一种体系结构，深度语义分割网络（DSSN）在语义分割任务上取得了一定程度的成功，显然比基于手工制作的功能优于传统方法。作为一种经典的数据驱动技术，DSSN可以通过端到端机制进行训练，并有能力采用低级和中级线索（即判别图像结构）来理解图像，但缺乏高级推理能力。相比之下，人类具有出色的推论能力，只有当人类掌握基本的RS领域知识时，才能可靠地解释RS图像。在文献中，本体论建模和推理是模仿和利用人类知识的理想方式，但在RS领域中仍然很少探索和采用。为了纠正上述DSSN的临界限制，本文提出了一个协作的增强框架（CBF），以迭代方式将数据驱动的深度学习模块和知识引导的本体论推理模块结合在一起。
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.
