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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.