Parametric adaptive modeling and detection for hyperspectral imaging

Hongbin Li, James H. Michels

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)II1057-II1060
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
StatePublished - 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004

Fingerprint

Dive into the research topics of 'Parametric adaptive modeling and detection for hyperspectral imaging'. Together they form a unique fingerprint.

Cite this