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 Experiment Videos

Feature reduction and morphological processing for hyperspectral image data.

David Casasent1, Xue-Wen Chen

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. casasent@ece.cmu.edu

Applied Optics
|January 23, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Micheliolide directly targets keratinocyte Annexin A2 to abrogate STAT3-driven inflammation and pruritus in atopic dermatitis.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Neuronal pentraxin 2 in peripheral sensory neurons drives chronic itch through potentiation of the interleukin-31/interleukin-31 receptor pathway in atopic dermatitis.

International immunopharmacology·2026
Same author

On-chip quantum interference of indistinguishable single photons from integrated independent molecules.

Nature nanotechnology·2025
Same author

Value of Diagnosis and Management for Skin Lesions by Integration of High-Frequency Ultrasound in Daily Practices: A Prospective Study.

Dermatology (Basel, Switzerland)·2025
Same author

Optimizing benefits of intensive SBP control in type 2 diabetes: the crucial role of angiotensin-converting enzyme inhibitors/angiotensin-II type 2 receptor blockers.

Journal of hypertension·2025
Same author

Bilateral meibomian gland morphological alterations in unilateral herpes simplex keratitis based on artificial intelligence analysis.

International journal of ophthalmology·2024
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
See all related articles

A new hyperspectral (HS) imaging system enhances automatic target recognition. This system uses a novel feature extraction algorithm to improve detection of objects like mines and vehicles in HS images.

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Signal Processing

Background:

  • Hyperspectral (HS) imagery offers rich spatial and spectral information for detailed object description.
  • Automatic target recognition (ATR) benefits from advanced data analysis techniques for improved performance.
  • Existing feature reduction methods may not fully exploit the high-dimensional nature of HS data.

Purpose of the Study:

  • To propose an automatic target detection system utilizing hyperspectral imagery.
  • To introduce a novel high-dimensional generalized discriminant feature-extraction algorithm for HS data.
  • To evaluate the proposed algorithm's effectiveness in target detection applications.

Main Methods:

  • Development of a new high-dimensional generalized discriminant feature-extraction algorithm.

Related Experiment Videos

  • Comparison of the proposed algorithm with existing feature-reduction methods.
  • Application of a nearest-neighbor classifier for target detection.
  • Simultaneous optimization of spatial and spectral responses in HS imagery.

    • Main Results:

    • The proposed feature-extraction algorithm demonstrated superior performance in discriminating targets within HS images.
    • Effective feature selection was achieved, leading to generalized detection capabilities.
    • The system showed promise in two specific HS target detection applications: mine and vehicle detection.

    Conclusions:

    • The developed hyperspectral automatic target detection system, incorporating a novel feature extraction method, is effective for identifying targets.
    • The high-dimensional generalized discriminant feature-extraction algorithm offers significant advantages over traditional methods for HS data analysis.
    • Simultaneous optimization of spatial and spectral information enhances the overall target detection performance.