Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

Related Experiment Video

Updated: Jun 29, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Automated target detection system for hyperspectral imaging sensors.

Marc A Kolodner1

  • 1Space Department, Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland 20723, USA. marc.kolodner@jhuapl.edu

Applied Optics
|October 3, 2008
PubMed
Summary

This study presents an automated hyperspectral target detection method. It uses a library-based approach with weather data for real-time, platform-based analysis without atmospheric compensation.

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

Related Experiment Videos

Last Updated: Jun 29, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals
07:24

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

Published on: April 14, 2020

Area of Science:

  • Remote Sensing
  • Spectroscopy
  • Data Science

Background:

  • Hyperspectral sensor technology has advanced, enabling real-time processing for operational use.
  • Robust and automated algorithms are crucial for supporting these advanced sensors.
  • Current methods often require extensive post-acquisition atmospheric correction.

Purpose of the Study:

  • To develop and validate an automated, robust target detection algorithm for hyperspectral imagery.
  • To enable immediate, platform-based detection without post-acquisition atmospheric compensation.
  • To predict target radiance signatures under various illumination conditions.

Main Methods:

  • A three-phase approach: library generation, data acquisition, and automated detection.
  • Projecting target reflectance signatures to the at-sensor radiance domain using weather data (forecast or radiosonde).
  • Implementing automated filters, adaptive thresholding, and confidence assignments for optimal information extraction.

Main Results:

  • The developed method allows for immediate, platform-based detection post-acquisition.
  • The approach successfully predicts radiance signatures under diverse illumination conditions.
  • Prototype software applied to 50 hyperspectral datacubes demonstrated detection performance across varied conditions.

Conclusions:

  • The automated, library-based hyperspectral detection method is effective and robust.
  • Eliminating the need for atmospheric compensation significantly enhances operational efficiency.
  • This approach offers a reliable solution for real-time target detection in diverse environments.