@inproceedings{bb388f86691546ee97a7ccb4240d1165,
title = "HSKL: A Machine Learning Framework for Hyperspectral Image Analysis",
abstract = "A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL's structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties.",
keywords = "Hyperspectral Imaging, Image classification, Machine Learning",
author = "Qian Cao and Deependra Mishra and John Wang and Steven Wang and Helena Hurbon and Berezin, {Mikhail Y.}",
note = "Funding Information: The team acknowledges funding from NSF 1827656 (MB), NSF 1355406 (MB), NIH R01 CA208623 and Mallinckrodt Institute of Radiology at Washington University (HH, QC). We also thank Optical Spectroscopy Core Facility at Washington University funded through NIH award 1S10RR03162. Publisher Copyright: {\textcopyright} 2021 IEEE.; 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 ; Conference date: 24-03-2021 Through 26-03-2021",
year = "2021",
month = mar,
day = "24",
doi = "10.1109/WHISPERS52202.2021.9483968",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2021 11th Workshop on Hyperspectral Imaging and Signal Processing",
}