Data for: Identifying Sunflower Lodging Based on Image Fusion and Deep Semantic Segmentation with UAV Remote Sensing Imaging

Supplementary material for the paper "Identifying Sunflower Lodging Based on Image Fusion and Deep Semantic Segmentation with UAV Remote Sensing Imaging". This dataset includes some core codes of the paper and remote sensing images for the study area. Among them, The "core code" folder contains three kinds of deep learning source code: FCN, SegNet, and our improved SegNet. The "Figure" folder contains illustrations of the original resolution of figures 4-6 in the paper. The "remote sensing" folder contains four subfolders: "Annotation" folder, "Multispectral images" folder, "Visible images" folder, "Fusion results of remote sensing images" folder. They store the results of manual annotation of remote sensing images by LabelMe, multispectral remote sensing images, visible remote sensing images, and the fusion results of visible and multispectral remote sensing images of two experimental fields, respectively.

本数据集为论文《基于图像融合与深度学习语义分割的无人机（UAV）遥感成像向日葵倒伏识别》的补充材料。本数据集包含该论文的部分核心代码与研究区域遥感影像。其中，"核心代码"文件夹包含三类深度学习源代码：全卷积网络（FCN）、SegNet以及本文改进的SegNet。"插图"文件夹收录了论文中图4至图6的原始分辨率插图文件。"遥感"文件夹下设四个子文件夹："标注"（Annotation）文件夹、"多光谱影像"（Multispectral images）文件夹、"可见光影像"（Visible images）文件夹以及"遥感影像融合结果"（Fusion results of remote sensing images）文件夹，上述子文件夹分别存储了通过LabelMe工具对遥感影像进行人工标注得到的标注结果、多光谱遥感影像、可见光遥感影像，以及两个试验田的可见光与多光谱遥感影像融合成果。