NDLI: Neural network ensemble and support vector machine classifiers for the analysis of remotely sensed data: a comparison

Content Provider	IEEE Xplore Digital Library
Author	Pasquariello, G. Ancona, N. Blonda, P. Tarantino, C. Satalino, G. D'Addabbo, A.
Copyright Year	2002
Description	Author affiliation: CNR-I.E.S.I., Bari, Italy (Pasquariello, G.; Ancona, N.; Blonda, P.; Tarantino, C.; Satalino, G.; D'Addabbo, A.)
Abstract	This paper presents a comparative evaluation between a classification strategy based on the combination of the outputs of a neural (NN) ensemble and the application of Support Vector Machine (SVM) classifiers in the analysis of remotely sensed data. Two sets of experiments have been carried out on a benchmark data set. The first set concerns the application of linear and non linear techniques to the combination of the outputs of a Multilayer Perceptron (MLP) neural network ensemble. In particular, the Bayesian and the error correlation matrix approaches are used for coefficient selection in the linear combination of the network's outputs. A MLP module is used for the non linear outputs combination. The results of linear and non linear combination schemes are compared and discussed versus the performance of SVM classifiers. The comparative analysis evidences that the nonlinear, MLP based, combination provides the best results among the different combination schemes. On the other hand, better performance can be obtained with SVM classifiers. However, the complexity of the SVM training procedure can be considered a limitation for SVMs application to real-world problems.
Sponsorship	IEEE IEEE Geosci. & Remote Sensing Soc. Canadian Remote Sensing Soc. Univ. Waterloo Natural Resources Canada (NRCan) Canadian Space Agency (CSA) Environ. Canada NASA Nat. Oceanic & Atmos. Adm. (NOAA) Office of Naval Res.(ONR) Nat. Space Dev. Agency of Japan (NASDA) Nat. Polar-orbiting Environ. Satellite Syst. (NPOESS)
Starting Page	509
Ending Page	511
File Size	281624
Page Count	3
File Format	PDF
ISBN	078037536X
DOI	10.1109/IGARSS.2002.1025089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-06-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Support vector machines Support vector machine classification Bayesian methods Multilayer perceptrons Multi-layer neural network Object detection Text categorization Robustness Mean square error methods
Content Type	Text
Resource Type	Article

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