Principal component analysis for change detection on polarimetric multitemporal SAR data

Author

Baronti, S. ; Carlà, R. ; Sigismondi, S. ; Alparone, L.

Author_Institution

Istituto di Ricerca sulle Onde Elettromagnetiche, CNR, Florence, Italy

Volume

4

fYear

1994

fDate

8-12 Aug 1994

Firstpage

2152

Abstract

Principal component analysis (PCA) is applied to investigate on changes occurring in multitemporal polarimetric SAR imagery. Correlation instead of covariance matrix is used in the transformation, thus reducing gain variations introduced by the imaging system and giving equal weight to each polarization. The approach is effective when PCA is computed on images recorded simultaneously, as well as when it is applied to the whole set of multitemporal images

Keywords

geophysical signal processing; geophysical techniques; image sequences; radar applications; radar imaging; radar polarimetry; remote sensing by radar; spaceborne radar; synthetic aperture radar; PCA; SAR imagery; SAR imaging; change detection; correlation; geophysical measurement technique; image processing; image sequences; land surface terrain mapping; multitemporal SAR data; polarization; principal component analysis; radar polarimetry; radar remote sensing; synthetic aperture radar; Covariance matrix; Data analysis; Eigenvalues and eigenfunctions; Image coding; Infrared detectors; Noise reduction; Polarization; Principal component analysis; Signal to noise ratio; Speckle;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International

Conference_Location

Pasadena, CA

Print_ISBN

0-7803-1497-2

Type

conf

DOI

10.1109/IGARSS.1994.399678

Filename

399678