  Visual pattern recognition over agricultural areas is an important
application of aerial image processing. In this paper, we consider the
multi-modality nature of agricultural aerial images and show that naively
combining different modalities together without taking the feature divergence
into account can lead to sub-optimal results. Thus, we apply a Switchable
Normalization block to our DeepLabV3 segmentation model to alleviate the
feature divergence. Using the popular symmetric Kullback Leibler divergence
measure, we show that our model can greatly reduce the divergence between RGB
and near-infrared channels. Together with a hybrid loss function, our model
achieves nearly 10\% improvements in mean IoU over previously published
baseline.
