论文信息 - A novel technique for hyperspectral signal subspace estimation in target detection applications

A novel technique for hyperspectral signal subspace estimation in target detection applications

This paper deals with the problem of signal subspace estimation and dimensionality reduction (DR) in hyperspectral images. A new algorithm is presented which preserves both the abundant and the rare signal components and is therefore suitable for DR in target detection applications. Results obtained by applying the new procedure and a classical method based on the analysis of the second order statistics are presented and discussed with reference to real AVIRIS data.

Stefania Matteoli | Marco Diani | Giovanni Corsini | Nicola Acito | Salvatore Resta

[1] John F. Arnold,et al. Reliably estimating the noise in AVIRIS hyperspectral images , 1996 .

[2] J. B. Lee,et al. Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[3] P. Switzer,et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[4] Y. Selen,et al. Model-order selection: a review of information criterion rules , 2004, IEEE Signal Processing Magazine.

[5] Charles W. Therrien,et al. Discrete Random Signals and Statistical Signal Processing , 1992 .

[6] E. Oja,et al. Independent Component Analysis , 2001 .

[7] Chein-I Chang,et al. Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8] John F. Mustard,et al. Spectral unmixing , 2002, IEEE Signal Process. Mag..

[9] David Malah,et al. Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals With Preservation of Rare Vectors , 2007, IEEE Transactions on Signal Processing.