Abstract
In this paper, an evolving neural network classifier using genetic simulated annealing algorithms (GSA) and its application to multi-spectral image classification is investigated. By means of GSA, the classifier presented is available to automatically evolve the appropriate architecture of neural network and find a near-optimal set of connection weights globally. Then, with Back-Propagation (BP) algorithm, the conformable connection weights for multi-spectral image classification can be found. The GSA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data effectively. The simulation results demonstrated that GSA-BP classifier possesses better performance on multi-spectral image classification. Its overall accuracy is improved by 4%~6% than conventional classifiers.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bandyopadhyay, S., Murthy, C.A., Pal, S.K.: Pattern Classification Using Genetic Algorithms: Determination of H. Pattern Recognition Letters 19, 1171–1181 (1998)
Benediktsson, J.A., Swain, P.H., Ersoy, O.K.: Neural Network Approaches versus Statistical Methods in Classification of Multi Source Remote Sensing Data. IEEE Transaction on Geo-science and Remote Sensing 28, 540–552 (1990)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. China Machine Press, Beijing (2001)
Filippi, A.M., Jensen, J.R.: Fuzzy Learning Vector Quantization for Hpyerspectral Coastal Vegetation Classification. Remote Sensing of Environment 100, 512–530 (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Hertz, J., Krogh, A., Palmer, R.: An Introduction to the Theory of Neural Computation. Addison Wesley Publ. Comp., Redwood City (1991)
Liu, Z.J., Wang, C.Y., Liu, A.X., Niu, Z.: Evolving Neural Network Using Real Coded Genetic Algorithm (GA) for Multi-spectral Image Classification. Future Generation Computer Systems 20, 1119–1129 (2004)
Pal, S.K., Bandyopadhyay, S., Murthy, C.A.: Genetic Classifiers for Remotely Sensed Images: Comparison with Standard Methods. International Journal of Remote Sensing 22, 2545–2569 (2001)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-propagating Errors. Nature 323, 533–536 (1986)
Tou, T.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, New York (1974)
Van Coillie, F.M.B., Verbeke, L.P.C., De Wulf, R.R.: Previously Trained Neural Networks as Ensemble Members: Knowledge Extraction and Transfer. International Journal of Remote Sensing 25, 4843–4850 (2004)
Van Coillie, F.M.B., Verbeke, L.P.C., De Wulf, R.R.: Feature Selection by Genetic Algorithms in Object-Based Classification of IKONOS Imagery for Forest Mapping in Flanders. Belgium, Remote Sensing of Environment 110, 476–487 (2007)
Van Rooij, A.J.F., Jain, L.C., Johnson, R.P.: Neural Network Training Using Genetic Algorithm. World Scientific Publishing, Co., Inc., River Edge (1996)
Yao, X.: Evolving Artificial Neural Networks. Proceeding of the IEEE 87, 1423–1447 (1999)
Yao, X., Xu, Y.: Recent Advances in Evolutionary Computation. Journal of Computer Science and Technology 21, 1–18 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fu, X., Guo, C. (2008). Evolving Neural Network Using Genetic Simulated Annealing Algorithms for Multi-spectral Image Classification. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-87734-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
eBook Packages: Computer ScienceComputer Science (R0)