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Change Detection Using Spectral Unmixing and IEA(Iterative Error Analysis) for Hyperspectral Images
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 5,  2015, pp.361-370
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.5.1
 Title & Authors
Change Detection Using Spectral Unmixing and IEA(Iterative Error Analysis) for Hyperspectral Images
Song, Ahram; Choi, Jaewan; Chang, Anjin; Kim, Yongil;
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 Abstract
Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.
 Keywords
hyperspectral;change detection;unmixing;IEA;
 Language
Korean
 Cited by
1.
An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles, Journal of Sensors, 2017, 2017, 1687-7268, 1  crossref(new windwow)
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