用无人机采集蓝莓田数据，改进U-Net实现更准的高光谱解混。
Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture
- 基于U-Net架构设计新型高光谱解混网络
- 在自建蓝莓田数据集上提升解混精度
- 适合农业遥感与高光谱图像处理研究者
高光谱解混是一种从高光谱数据立方体像素中提取物质（通常称为端元）及其丰度信息的算法。由于高光谱传感器空间分辨率较低，每个像素可能包含多个端元的混合信息。本文基于安装在无人机上的高光谱相机采集的蓝莓田数据，构建了一个新的高光谱解混数据集，并提出一种基于U-Net网络架构的高光谱解混算法，在现有及新构建的数据集上均实现了更精确的解混结果。
原文摘要 · Abstract (English)
The hyperspectral unmixing method is an algorithm that extracts material (usually called endmember) data from hyperspectral data cube pixels along with their abundances. Due to a lower spatial resolution of hyperspectral sensors data in each of the pixels may contain mixed information from multiple endmembers. In this paper we create a hyperspectral unmixing dataset, created from blueberry field data gathered by a hyperspectral camera mounted on a UAV. We also propose a hyperspectral unmixing algorithm based on U-Net network architecture to achieve more accurate unmixing results on existing and newly created hyperspectral unmixing datasets.
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