Target detection with multiple reflection linear unmixing for hyperspectral remote sensing imagery

Hsuan Ren, Shih Min Hsu, Chiu Yu Liu

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

Abstract

Linear mixture model has been widely used for abundance estimation in remotely sensed imagery. Since each pixel in the remote sensing images usually covers several meters on the ground and contains more than one material, its spectrum can be considered as a mixture of all the materials residents in that pixel. Linear mixture model simply assumes this mixture is linear and then estimates the abundance fraction by least square approaches. If the surface is smooth, linear mixture can approximately fit the spectrum. However, if the surface is rough, the multiple reflection effect needs to be considered. In this study, we propose a multiple reflection linear mixture which not only consider single reflection linear mixture, but also includes double and triple reflections. In the model, we also adopt one single factor for the probability of multiple reflections or the roughness. The preliminary result shows the multiple reflection linear mixture can fit the spectrum with less error comparing to traditional linear mixture assumption.

Original languageEnglish
Title of host publication33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Pages1461-1464
Number of pages4
StatePublished - 2012
Event33rd Asian Conference on Remote Sensing 2012, ACRS 2012 - Pattaya, Thailand
Duration: 26 Nov 201230 Nov 2012

Publication series

Name33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Volume2

Conference

Conference33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Country/TerritoryThailand
CityPattaya
Period26/11/1230/11/12

Keywords

  • Hyperspectral
  • Least Square
  • Linear Unmixing
  • Multiple Reflection
  • Target Detection

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