Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery

• URL: http://arxiv.org/abs/2010.02451v1
• Date: Tue, 6 Oct 2020 03:32:17 GMT
• Title: Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery
• Authors: Yansheng Li, Song Ouyang, and Yongjun Zhang
• Abstract summary: DSSN can be trained by an end-to-end mechanism and competent for employing the low-level and mid-level cues. Human beings have an excellent inference capacity and can be able to reliably interpret the RS imagery. This paper proposes a collaboratively boosting framework (CBF) to combine data-driven deep learning module and knowledge-guided ontological reasoning module.
• Score: 2.342488890032597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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