Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture
This work addresses hyperspectral unmixing for agricultural monitoring, but it is incremental as it adapts an existing U-Net architecture to a specific domain.
The paper tackled hyperspectral unmixing in agricultural images from UAVs by proposing a U-Net-based algorithm and creating a new blueberry field dataset, achieving more accurate results on both existing and new datasets.
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.