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Soft Computing

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11.06.2015 | Focus

An efficient radial basis function neural network for hyperspectral remote sensing image classification

verfasst von: Jiaojiao Li, Qian Du, Yunsong Li

Erschienen in: Soft Computing | Ausgabe 12/2016

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Abstract

A very simple radial basis function neural network (RBFNN) is investigated for hyperspectral remote sensing image classification. Its training can be analytically solved with a closed-form equation, and no parameter needs to be manually tuned. Its computational cost is much lower than the popular support vector machine (SVM). Surprisingly, such an RBFNN can achieve the performance that is similar to or even better than the SVM. By incorporating a simple spatial averaging filter or a Gaussian lowpass filter with negligible additional computational cost, classification accuracy can be further improved. Considering the large matrix inversion operation in the RBFNN when the number of training samples being very large, we also propose a parallel processing method to reduce computing time in matrix inversion.

Vorheriger Artikel Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery

Nächster Artikel Atomicity via regularity for non-additive set multifunctions

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Titel: An efficient radial basis function neural network for hyperspectral remote sensing image classification
verfasst von: Jiaojiao Li
Qian Du
Yunsong Li
Publikationsdatum: 11.06.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 12/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1739-9

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