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

Author

Acito, N. ; Corsini, G. ; Diani, M. ; Matteoli, S. ; Resta, S.

Author_Institution

Dept. of Ing. dell´´Inf., Univ. of Pisa, Pisa

Volume

3

fYear

2008

fDate

7-11 July 2008

Abstract

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.

Keywords

geophysical signal processing; geophysical techniques; object detection; remote sensing; AVIRIS; AVIRIS data; DR; HFC; Harsanyi-Farrand-Chang; MDL; Minimum Description Length; NSP algorithm; Noise Subspace Projection; RSSE algorithm; Robust Signal Subspace Estimation; dimensionality reduction; hyperspectral image; target detection application; Higher order statistics; Hybrid fiber coaxial cables; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Object detection; Pixel; Principal component analysis; Signal processing algorithms; Statistical analysis; Dimensionality reduction; signal rank estimation; signal subspace estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International

Conference_Location

Boston, MA

Print_ISBN

978-1-4244-2807-6

Electronic_ISBN

978-1-4244-2808-3

Type

conf

DOI

10.1109/IGARSS.2008.4779291

Filename

4779291