2002/11/01 by Andreas F. Hayden, David R. Twede · 8 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Calibration and Measurement Techniques #Computer science #Covariance #Covariance matrix #Eigenvalues and eigenvectors #Hyperspectral imaging #Image and Signal Denoising Methods #Mathematics #Noise (video) #Noise measurement #Noise reduction #Pattern recognition (psychology) #Physics #Remote-Sensing Image Classification #Spectral space #Statistics
paper · doi:10.1117/12.453777
published in Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE 4816, 355 (SPIE)
openalex publication_date 2002/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Hyperspectral data rarely spans the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation introduced by data processing. Standard exploitation of data, which often does not consider the possibility of a reduced band space, leads to reduced detection performance. Spectral signature detection performance can be improved by estimating the covariance on a subset of the band space components. The decision about how to limit the band space can be determined by factors such as in-scene estimation of noise. In-scene estimation of noise can be used to optimize spectral signature detection when spectral filtering methods based on covariance inverses are used. We present here a method for determining instrument noise and a new method of covariance inverse regularization which increases spectral filtering performance.