基于卷积神经网络的生菜多光谱图像分割与配准

针对多光谱图像中由于多镜头多光谱相机各通道之间存在的偏差以及传统分割方法的不适用,图像分析处理过程往往会出现无法自动化分割或分割精度较低的问题,提出采用基于相位相关算法和基于UNet的语义分割模型对田间生菜多光谱图像进行各个通道的精确配准并实现前景分割。使用Canny算法对多光谱各通道图像进行边缘提取,进而使用相位相关算法对多光谱各通道图像进行配准,单幅图像平均处理时间0.92s,配准精度达到99%,满足后续图像分割所需精度;以VGG16作为主干特征提取网络,直接采用两倍上采样,使最终输出图像和输入图像高宽相等,构建优化的UNet模型。实验结果表明：本文所提出的图像配准和图像分割网络,分割像素准确率达到99.19%,平均IoU可以达到94.98%,能够很好地对生菜多光谱图像进行前景分割,可以为后续研究作物精准表型的光谱分析提供参考。

Segmentation and Registration of Lettuce Multispectral Image Based on Convolutional Neural Network

In view of the deviations between the channels of multi-lens multi-spectral cameras and the inapplicability of traditional segmentation methods in multi-spectral images, the image analysis and processing process often has the problem of inability to automate segmentation or low segmentation accuracy, so a phase-based algorithm was proposed. And the semantic segmentation model based on UNet performs accurate registration of each channel of the field lettuce multispectral image and realizes foreground segmentation. The Canny algorithm was used to extract the edges of the multi-spectral channel images, and then the phase correlation algorithm was used to register the multi-spectral channel images. The average processing time of a single image was 0.92s, efficiency was increased by 40%, and the registration accuracy reached 99%, which met the requirements of subsequent images and the required accuracy of segmentation. VGG16 was used as the backbone feature extraction network, and the double up sampling was directly used to make the final output image and the input image equal in height and width, and the optimized UNet model was constructed. The experimental results showed that the image registration and image segmentation network proposed achieved 99.19% pixel accuracy and an average IoU of 94.98%. It can perform foreground segmentation on lettuce multispectral images very well, which can be used for follow-up spectral analysis to study the precise phenotype of crops.