Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

1 Publication

Hyperspectral Image Analysis: From conventional model-based algorithms towards deep learning data-driven ones

Rasti, B.


Hyperspectral image analysis has considerably evolved during the past decades. The conventional model-based image processing and machine learning techniques are not efficient for hyperspectral image analysis, therefore, other advanced models such as spatial- spectral models were proposed to boost the hyperspectral analysis. Recent advances in ma- chine learning i.e., deep learning (DL) confirm that if an adequate amount of training data is supplied then DL-based algorithms outperform the conventional (shallow) ones. However, the available training data is often limited in hyperspectral imaging, therefore, the advan- tage of DL-based algorithms compared with shallow ones remains an open question. In this paper, we address this issue for two vibrant fields in hyperspectral image analysis i.e., Unmixing and Feature Extraction for Classification.

  • Invited lecture (Conferences) (Online presentation)
    OSA Optical Sensors and Sensing Congress 2021, 21.07.2021, Virtual, Virtual


Years: 2023 2022 2021 2020 2019 2018 2017 2016 2015