Greedy modular subspace segment principle component analysis

Hsin Ting Chen, Hsuan Ren, Yang Lang Chang

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


Hyperspectral images collect hundreds of co-registered images of the earth surface with different wavelengths in visible and short-wave inferred region. With such high spectral resolution, many adjacent bands are highly correlated, i.e., they contain a lot redundant information. How to remove unnecessary information from this huge amount of data and preserve all the information is a challenging problem. Principal component analysis (PCA) is one of the widely used algorithms for this problem. It assumes the larger variance contains the most information, so it projects the data into the direction to maximize the variance. Most of the signals will be kept in the first several principal components, and the rest will be considered to be noise and neglected. To further reduce the redundancy, segment PCA is proposed, which first separate the whole spectral bands into blocks and then perform the original PCA in each block individually. Both these two approaches perform well for data compression, but for image classification in its feature space, they did not achieve comparable results. In this study, we adopt the greedy modular subspaces transformation (GMST) to find the optimal feature subspace for the segment PCA. It is expected to provide a comparable classification results with high compression performance.

Original languageEnglish
Title of host publicationChemical and Biological Sensors for Industrial and Environmental Monitoring III
StatePublished - 2007
EventChemical and Biological Sensors for Industrial and Environmental Monitoring III - Boston, MA, United States
Duration: 11 Sep 200712 Sep 2007

Publication series

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


ConferenceChemical and Biological Sensors for Industrial and Environmental Monitoring III
Country/TerritoryUnited States
CityBoston, MA


  • Greedy Modular Subspaces Transformation (GMST)
  • Hyperspectral imagery
  • Segment principal component analysis


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