Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis

John Stamford, Seher Bahar Aciksoz, Tracy Lawson

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages373-390
Number of pages18
DOIs
StatePublished - 2024
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume2790
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Hyperspectral
  • Machine learning
  • Modeling
  • Partial least squares
  • Phenotyping
  • PLS
  • Traits

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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