HSKL: A Machine Learning Framework for Hyperspectral Image Analysis

Qian Cao, Deependra Mishra, John Wang, Steven Wang, Helena Hurbon, Mikhail Y. Berezin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2021 11th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665436014
DOIs
StatePublished - Mar 24 2021
Event11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 - Amsterdam, Netherlands
Duration: Mar 24 2021Mar 26 2021

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2021-March
ISSN (Print)2158-6276

Conference

Conference11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021
Country/TerritoryNetherlands
CityAmsterdam
Period03/24/2103/26/21

Keywords

  • Hyperspectral Imaging
  • Image classification
  • Machine Learning

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