Signature reduction methods for target detection in multispectral remote sensing imagery

Hsuan Ren, Jyh Perng Fang, Yang Lang Chang

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

Abstract

Multispectral sensors are still widely used in satellite remote sensing. They usually have spectral bands less than ten channels. The problem for so few channels is that it can not directly solve linear mixture model by least square unmixing for subpixel target detection. In order for least square approach to be effective, the number of bands must be greater than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such constraint is not an issue for hyperspectral images since they generally have hundreds of bands, however, this may not be true for multispectral images where the number of signatures to be classified might be greater than the number of bands. In order to relax this constraint, we present two signature reduction methods to reduce the number of unknowns, based on signature selection and signature fusion. A SPOT image scene will be used for experiment to demonstrate the performance.

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 Number Constraint (BNC)
  • Least square
  • Multispectral
  • Signature fusion
  • Signature selection

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