Reducing the Feature Divergence of RGB and Near-Infrared Images Using Switchable Normalization

Siwei Yang, Shaozuo Yu, Bingchen Zhao, Yin Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 46-47

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 SwitchableNormalization block to ourDeepLabV3+ 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.