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Visual pattern recognition over agricultural areas is an importantapplication of aerial image processing. In this paper, we consider themulti-modality nature of agricultural aerial images and show that naivelycombining different modalities together without taking the feature divergenceinto account can lead to sub-optimal results. Thus, we apply a SwitchableNormalization block to our DeepLabV3 segmentation model to alleviate thefeature divergence. Using the popular symmetric Kullback Leibler divergencemeasure, we show that our model can greatly reduce the divergence between RGBand near-infrared channels. Together with a hybrid loss function, our modelachieves nearly 10\% improvements in mean IoU over previously publishedbaseline.

Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization