Classifying reflectance targets under ambient light conditions using passive spectral measurements

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1 Scopus citations


Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

Original languageEnglish
Article number5375
Pages (from-to)1-20
Number of pages20
JournalSensors (Switzerland)
Issue number18
StatePublished - Sep 2 2020

Bibliographical note

Funding Information:
This work was supported in part by the USDA National Institute of Food and Agriculture, Multistate Project S1069 (Accession No. 1015710). This work was also supported in part by the National Science Foundation under Grant No. 1539070, Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics (CLOUD−MAP), to Oklahoma State University in partnership with the University of Oklahoma, University of Nebraska−Lincoln, and the University of Kentucky.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Ambient light compensation
  • Machine learning
  • Reflectance target classification
  • Remote sensing
  • Spectroscopy

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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