Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International
Conference_Location :
Hamburg
Print_ISBN :
0-7803-5207-6
DOI :
10.1109/IGARSS.1999.771551
