Semi-supervised hyperspectral unmixing dataset creation methods for unmixing algorithm analysis

Hyperspectral imaging is part of a growing remote sensing industry used in various applications like food or agriculture industries. Labeling hyperspectral data cubes is a resource and time intensive task. In order to try and speed up the labeling procedure, we propose a semi-supervised machine learning methodology to improve labeling speed at a cost of computational resources. An experiment was created to test the viability of this methodology. Gathered results show low hyperspectral label prediction (classification) accuracy using simple and fast neural networks.