Hyperspectral image segmentation, deblurring, and spectral analysis for material identification

Fang Li, Kwok Po NG, Robert Plemmons*, Sudhakar Prasad, Qiang Zhang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

15 Citations (Scopus)


An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Enabling technologies include image enhancement, segmentation and spectral trace recovery. Since multi-spectral or hyperspectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. Also, noise and blur can present significant data analysis problems. In this paper, we first describe a variational fuzzy segmentation model coupled with a denoising/deblurring model for material identification. A statistical moving average method for segmentation is also described. These new approaches are then tested and compared on hyperspectral images associated with space object material identification.

Original languageEnglish
Title of host publicationVisual Information Processing XIX
Publication statusPublished - 2010
EventVisual Information Processing XIX - Orlando, FL, United States
Duration: 6 Apr 20107 Apr 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceVisual Information Processing XIX
Country/TerritoryUnited States
CityOrlando, FL

Scopus Subject Areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Classification
  • Deblurring
  • Denoising
  • Dimensionality reduction
  • Hyperspectral data
  • Segmentation
  • Spectral mixture analysis


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