Applications of Kalman Filtering to Single Hyperspectral Signature Analysis

Su Wang, Chuin Mu Wang, Mann Li Chang, Ching Tsorng Tsai, Chein I. Chang, Chein I. Chang

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Kalman filter (KF) is a widely used statistical signal processing technique for parameter estimation. Recently, a KF-based approach to linear spectral unmixing, called KF-based linear spectral unmixing (KFLU) was developed for mixed pixel classification. However, its applicability to spectral characterization for spectral estimation, identification, and quantification has not been explored. This paper presents new applications of Kalman filtering in spectral estimation, identification and abundance quantification for which three KF-based spectral characterization signal processing techniques are developed. These techniques are completely different from the KFLU in the sense that the former performs a KF across a spectral coverage wave-length by wavelength as opposed to the latter, which implements a Kalman filter pixel vector by pixel vector throughout an entire image cube. In addition, the proposed KF-based techniques do not require a linear mixture model as KFLU does. Accordingly, they are not linear spectral unmixing methods, but rather spectral signature filters operating as if they are spectral measures.

Original languageEnglish
Pages (from-to)547-563
Number of pages17
JournalIEEE Sensors Journal
Issue number3
Publication statusPublished - 2010 Mar

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


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