A simulated annealing band selection approach for hyperspectral imagery

Jyh Perng Fang, Yang Lang Chang, Hsuan Ren, Chun Chieh Lin, Wen Yew Liang, Jwei Fei Fang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

For hyperspectral imagery, greedy modular eigenspaces (GME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modules based on greedy algorithm. Instead of greedy paradigm as adopted in GME approach, this paper introduces a simulated annealing band selection (SABS) approach for hyperspectral imagery. SABS selects sets of non-correlated hyperspectral bands for hyperspectral images based on simulated annealing (SA) algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique simulated annealing module eigenspace (SAME) feature. The proposed SABS features: (1) avoiding the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis (PCA); (2) selecting each band by a simple logical operation, call SAME feature scale uniformity transformation (SAME/FSUT), to include different classes into the most common feature clustered subset of bands; (3) providing a fast procedure to simultaneously select the most significant features according to SA scheme. The experimental results show that our proposed SABS approach is effective and can be used as an alternative to the existing band selection algorithms.

Original languageEnglish
Title of host publicationChemical and Biological Sensors for Industrial and Environmental Monitoring II
DOIs
StatePublished - 2006
EventChemical and Biological Sensors for Industrial and Environmental Monitoring II - Boston, MA, United States
Duration: 3 Oct 20064 Oct 2006

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6378
ISSN (Print)1605-7422

Conference

ConferenceChemical and Biological Sensors for Industrial and Environmental Monitoring II
Country/TerritoryUnited States
CityBoston, MA
Period3/10/064/10/06

Keywords

  • Band selection
  • Feature scale uniformity transformation (FSUT)
  • Hyperspectral
  • Principal component analysis (PCA)
  • Simulated annealing band selection (SABS)
  • Simulated annealing module eigenspace (SAME)

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