@inproceedings{a9462acd9605456a99216823809891d0,
title = "INVESTIGATING THE IMPACT OF THE TRAINING SET SIZE ON DEEP LEARNING-POWERED HYPERSPECTRAL UNMIXING",
abstract = "Hyperspectral unmixing allows us to estimate the endmember abundances in each pixel of an input hyperspectral image. Although there exist deep learning-powered end-to-end methods for this task, the lack of labeled ground-truth data is a challenging problem which makes the adoption of such techniques extremely difficult in emerging practical use cases where the ground truth is costly to capture. In this paper, we investigate and quantify the impact of the training set size on the quality of unmixing provided by deep learning models of conceptually different architectures.",
keywords = "Deep learning, Hyperspectral unmixing, Training set selection",
author = "Lukasz Tulczyjew and Jakub Nalepa",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9553477",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2024--2027",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}