Color cast of the UAV imagery is inevitable due to the temperature and illuminance changes during the UAV flight. Since color is an important feature in crop monitoring, color cast often produces misleading inference in estimation of crop stress, nutrient, and productivity. Therefore, color calibration is a necessary step to remove the negative effects of color variation for UAV based crop monitoring. Nowadays, the state of art color calibration methods usually use the semantic correspondences for accurate color transfer. However, the mainstream color calibration methods ignore the integration of semantic segmentation and style transfer, and suffer the problem of semantic mismatch. To address this problem, this study proposed a multi decoder architecture that builds the integration of sematic segmentation and style transfer for the color transfer in an end to end mode. Also, this paper introduced an Crop Oriented adaptive instance normalization (AdaIN) method to estimate the color cast in the crop areas, and used that estimated information for color calibration over the whole image area with a local to global attention mechanism. Each proposed module was evaluated in ablation study to test its effectiveness, respectively. Also, the proposed method was evaluated on several crop types and compared with the state of art methods. Experimental results showed that our proposed method achieved state of art or close to state of art performance in all metrics. The research of this work is expected to obtain a general framework to remove the color cast of UAV imagery for crop monitoring, which may build a good foundation for the following data interpretation. Our dataset and codes are can be downloaded at http://github.com/huanghsheng/deep-color-calibration.

由于无人机飞行过程中的温度和照度变化，无人机图像的色偏是不可避免的。由于颜色是作物监测中的一个重要特征，因此在估计作物压力、养分和生产力时，色偏通常会产生误导性的推断。因此，颜色校准是消除基于无人机的作物监测颜色变化负面影响的必要步骤。如今，最先进的颜色校准方法通常使用语义对应来进行准确的颜色传输。然而，主流的颜色校准方法忽略了语义分割和风格迁移的整合，存在语义不匹配的问题。为了解决这个问题，本研究提出了一种多解码器架构，该架构将语义分割和样式传输集成在一起，以端到端模式进行颜色传输。此外，本文介绍了一种面向裁剪的自适应实例归一化 (AdaIN) 方法来估计裁剪区域中的偏色，并使用该估计信息通过局部到全局注意机制在整个图像区域上进行颜色校准。每个提议的模块都在消融研究中进行了评估，以分别测试其有效性。此外，所提出的方法在几种作物类型上进行了评估，并与最先进的方法进行了比较。实验结果表明，我们提出的方法在所有指标上都达到了最先进或接近最先进的性能。本工作的研究有望获得用于作物监测的无人机图像偏色去除的通用框架，为后续数据解读奠定良好的基础。我们的数据集和代码可以在 http://github.com/huanghsheng/deep-color-calibration 下载。