Robust supervised classification algorithm for multivariate contaminated data based on modified M-estimates

Author

Jhung, Yonhong ; Swain, Philip H.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

fYear

1993

fDate

18-21 Aug 1993

Firstpage

1527

Abstract

Presents a robust Gaussian maximum likelihood (ML) classifier that makes use of the modified M-estimates of the first and second order statistics. The optimal solutions that minimize the modified Huber criterion replace the least squares (LS) estimates. A heuristic approach to deciding the threshold value that influences the estimates and overall classification accuracy is presented. The performance of the robust ML classifier is examined along with the conventional ML classifier for two common contamination cases

Keywords

geophysical techniques; geophysics computing; image recognition; remote sensing; Gaussian maximum likelihood classifier; Huber criterion; geophysical measurement technique; heuristic approach; image classification; land surface remote sensing; modified M-estimate; multivariate contaminated data; robust supervised classification algorithm; second order statistics; threshold value; Classification algorithms; Contamination; Least squares approximation; Maximum likelihood estimation; Remote sensing; Robustness; Statistical distributions; Statistics; Stochastic processes; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1993. IGARSS '93. Better Understanding of Earth Environment., International

Conference_Location

Tokyo

Print_ISBN

0-7803-1240-6

Type

conf

DOI

10.1109/IGARSS.1993.322760

Filename

322760