Statistical analysis and segmentation of multi-look SAR imagery using partial polarimetric data

Author

Lee, J.S. ; Du, L. ; Schuler, D.L. ; Grunes, M.R.

Author_Institution

Remote Sensing Div., Naval Res. Lab., Washington, DC, USA

Volume

2

fYear

34881

fDate

10-14 Jul1995

Firstpage

1422

Abstract

For terrain type classifications, when full polarimetric SAR data are not available, or when only selected discriminants are used, this paper presents customized maximum likelihood classification algorithms based on the probability density functions specifically developed for each case. It is found that in some cases, the use of partial information has actually improved the classification accuracy for some classes. The reason and its implications are discussed. NASA/JPL polarimetric SAR data are used for illustrations

Keywords

geophysical signal processing; geophysical techniques; image classification; image segmentation; radar imaging; radar polarimetry; remote sensing by radar; synthetic aperture radar; SAR imaging; customized maximum likelihood classification algorithm; geophysical measurement technique; image classification; image segmentation; land surface; multi-look SAR imagery; multilook radar; probability density function; radar polarimetry; radar remote sensing; statistical analysis; synthetic aperture radar; terrain mapping; Classification algorithms; Covariance matrix; Image segmentation; Instruments; Laboratories; NASA; Probability density function; Remote sensing; Statistical analysis; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and Applications', International

Conference_Location

Firenze

Print_ISBN

0-7803-2567-2

Type

conf

DOI

10.1109/IGARSS.1995.521768

Filename

521768