Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/69091
標題: Applications of Kalman Filtering to Single Hyperspectral Signature Analysis
作者: Wang, S.
Wang, C.M.
Chang, M.L.
Tsai, C.T.
Chang, C.I.
關鍵字: Kalman filter (KF);Kalman filter-based linear spectral unmixing (KFLU);Kalman filter-based spectral characterization signal processing;(KFSCSP);Kalman filter-based spectral signature identifier (KFSSI);Kalman filter-based spectral signature quantifier (KFSSQ);Kalman;filter-based spectral signature estimator (KFSSE);spectral estimation;spectral identification;spectral quantification;subspace projection approach;spectral mixture analysis;classification;images;models
Project: Ieee Sensors Journal
期刊/報告no:: Ieee Sensors Journal, Volume 10, Issue 3, Page(s) 547-563.
摘要: 
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 wavelength 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.
URI: http://hdl.handle.net/11455/69091
ISSN: 1530-437X
DOI: 10.1109/jsen.2009.2038546
Appears in Collections:期刊論文

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