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

Published in the Proceedings of IEEE CVPR Workshop on Agriculture-Vision, 2020

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Abstract

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 KullbackÔÇôLeibler 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.

Bibtex

@inproceedings{yang2020CVPRW,
  title={Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization},
  author={ {Siwei Yang, Shaozuo Yu, Bingchen Zhao} and Yin Wang},
  booktitle={Proceedings of IEEE CVPR(2020) Workshop on Agriculture-Vision},
  year={2020}
}