  Despite the good results that have been achieved in unimodal segmentation,
the inherent limitations of individual data increase the difficulty of
achieving breakthroughs in performance. For that reason, multi-modal learning
is increasingly being explored within the field of remote sensing. The present
multi-modal methods usually map high-dimensional features to low-dimensional
spaces as a preprocess before feature extraction to address the nonnegligible
domain gap, which inevitably leads to information loss. To address this issue,
in this paper we present our novel Imbalance Knowledge-Driven Multi-modal
Network (IKD-Net) to extract features from raw multi-modal heterogeneous data
directly. IKD-Net is capable of mining imbalance information across modalities
while utilizing a strong modal to drive the feature map refinement of the
weaker ones in the global and categorical perspectives by way of two
sophisticated plug-and-play modules: the Global Knowledge-Guided (GKG) and
Class Knowledge-Guided (CKG) gated modules. The whole network then is optimized
using a holistic loss function. While we were developing IKD-Net, we also
established a new dataset called the National Agriculture Imagery Program and
3D Elevation Program Combined dataset in California (N3C-California), which
provides a particular benchmark for multi-modal joint segmentation tasks. In
our experiments, IKD-Net outperformed the benchmarks and state-of-the-art
methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset.
IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC
2018 challenge evaluation until this paper's submission.
