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

Vytautas Paura; Virginijus Marcinkevičius

doi:10.1117/12.2679826

19 October 2023 Semi-supervised hyperspectral unmixing dataset creation methods for unmixing algorithm analysis

Vytautas Paura, Virginijus Marcinkevičius

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330Y (2023) https://doi.org/10.1117/12.2679826
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Conference Poster

Abstract

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.

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Vytautas Paura and Virginijus Marcinkevičius "Semi-supervised hyperspectral unmixing dataset creation methods for unmixing algorithm analysis", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330Y (19 October 2023); https://doi.org/10.1117/12.2679826

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