TY - JOUR
T1 - Parametric adaptive modeling and detection for hyperspectral imaging
AU - Li, Hongbin
AU - Michels, James H.
PY - 2004
Y1 - 2004
N2 - Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel. Traditional approaches to the so-called subpixel target signal detection problem involve the estimation of the sample covariance matrix of the background from target-free training pixels. This entails a large training requirement and high complexity. In this paper, we investigate parametric adaptive modeling and detection for HSI applications. To deal with non-stationarity in the spectral dimension that is characteristic of HSI data, we introduce a sliding-window based timevarying (TV) autoregressive (AR) modeling and detection technique, by which the spectral data is sliced into overlapping subvectors for parameter estimation and signal whitening. Experimental results using real HSI data show that the proposed parametric technique outperforms conventional detection schemes, especially when the training size is small.
AB - Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel. Traditional approaches to the so-called subpixel target signal detection problem involve the estimation of the sample covariance matrix of the background from target-free training pixels. This entails a large training requirement and high complexity. In this paper, we investigate parametric adaptive modeling and detection for HSI applications. To deal with non-stationarity in the spectral dimension that is characteristic of HSI data, we introduce a sliding-window based timevarying (TV) autoregressive (AR) modeling and detection technique, by which the spectral data is sliced into overlapping subvectors for parameter estimation and signal whitening. Experimental results using real HSI data show that the proposed parametric technique outperforms conventional detection schemes, especially when the training size is small.
UR - http://www.scopus.com/inward/record.url?scp=4644231839&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:4644231839
SN - 1520-6149
VL - 2
SP - II1057-II1060
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
T2 - Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing
Y2 - 17 May 2004 through 21 May 2004
ER -