  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.
