Hybrid consensus theoretic classification with pruning and regularization

Author

Benediktsson, Jon Atli ; Benediktsson, Kjartan

Author_Institution

Dept. of Electr. & Comput. Eng., Iceland Univ., Reykjavik, Iceland

Volume

5

fYear

1999

fDate

1999

Firstpage

2486

Abstract

Conventional statistical pattern recognition methods are not appropriate in classification of multisource data since such data cannot, in most cases, be modeled by a common convenient multivariate statistical model. However, methods based on consensus theory have shown potential in classification of multisource data. Here, optimized combination, regularization, and pruning is proposed for consensus theoretic classification. The regularization scheme iteratively adapts regularization parameters by minimizing the validation error

Keywords

geophysical signal processing; geophysical techniques; geophysics computing; image classification; neural nets; remote sensing; sensor fusion; terrain mapping; adaptive signal processing; consensus theory; geophysical measurement technique; hybrid consensus theoretic classification; image classification; image processing; iterative method; land surface; minimization; minimizing; multisource data; neural net; optimized combination; pruning; regularization; regularization scheme; remote sensing; sensor fusion; statistical pattern recognition; terrain mapping; validation error; Biological neural networks; Cost function; Councils; Graphics; Neural networks; Pattern recognition; Probability distribution; Redundancy; Remote sensing; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International

Conference_Location

Hamburg

Print_ISBN

0-7803-5207-6

Type

conf

DOI

10.1109/IGARSS.1999.771551

Filename

771551