Abstract
This paper introduces a new approach for neural network application to coastal studies. The method is based on the utilization of the Hopfield neural network to model sea surface current movements from single TiungSAT-1 image. In matching process using Hopfield neural network, identified features have to be mathematically compared to each other in order to build an energy function that will be minimized. In this context, the neuron network has been taken in two dimensions; raw and column in order to match between the similar features of surface pattern. It was required that the two features were extracted from the same location. The Euler method is used to minimized the energy function of neuron equation. The study shows that the surface current features such as structure morphology of water plume can be automatically detected. In TiungSAT-1 data, green and near-infrared bands were competent at sea surface current features detection with high accuracy speed of ±0.14 m/s. It can be said that, Hopfield neural network has highly promised feature enhancement and detection in optical satellite sensor such as TiungSAT-1 image. In conclusion, Hopfield neural network can be used advance computational tool for modeling the pattern movement of sea surface in satellite data.
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References
Alvin, L., Meng, S., Mazlan, H.: Classification of TiungSAT-1 MSEIS Data for Land Cover Mapping with Hyperspectral Analysis Approach. In: TiungSAT-1: Data Applications. Astronautic Technology (M) Sdn. Bhd., Bukit Jalil, Kuala Lumpur, pp. 50–60 (2003) ISBN 983-41363-0-7
Arik, S.: A note on the global stability of dynamical neural networks. IEEE Trans. Circuits Systems—I: Fund. Theory Appl. 49, 502–504 (2002)
Cao, J., Wang, J.: Global asymptotic stability of a general class of recurrent neural networks with time-varying delays. IEEE Trans. Circuits Systems—I: Fund. Theory Appl. 50(1), 34–44 (2003)
Côté, S., Tatnall, A.R.L.: The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images. International Journal of Remote Sensing 18(4), 871–885 (1997)
Juang, J.C.: Stability analysis of Hopfield-type neural networks. Neural Networks 10(6), 1365–1373 (1999)
Liang, X.B., Wang, J.: Absolute exponential stability of neural networks with a general class of activation functions. IEEE Trans. Circuits Systems—I: Fund. Theory Appl. 47(8), 1258–1263 (2000)
Maged, M., Mazlan, H.: Simulation of Oil Slick Trajectory Movements from the RADARSAT-1 SAR. Asian Journal of Geoinformatics 5(4), 17–27 (2005)
Mazlan, H., Hazli, H.: Radiometric and geometric information content of TiungSAT-1 MSEIS Data. In: TiungSAT-1: From Inception to Inauguration. Astronautic Technology (M) Sdn. Bhd., Bukit Jalil, Kuala Lumpur, pp. 185–200 (2001) ISBN 983-867-193-2
Mohd, I.S., Ming, L.C.: Assessment of the Capability of TiungSAT-1 Satellite Data for Mapping Chlorophyll Distribution. In: TiungSAT-1: Data Applications. Astronautic Technology (M) Sdn. Bhd., Bukit Jalil, Kuala Lumpur, pp. 44–49 (2003) ISBN 983-41363-0-7
MatJafri, M.Z., Abdullah, K., Lim., H.S.: Malaysian Tiungsat-1 Imagery for Water Quality Mapping (2003), http://www.gisdevelopment.net/application/nrm/water/quality/watq0005.htm
Nasrabadi, N.M., Choo, C.Y.: Hopfield network for stereo vision correspondence. IEEE transactions on neural networks 3(1), 5–13 (1992)
Robinson, I.S.: Satellite Oceanography An Introduction for Oceanographers and Remote Sensing Scientists, 2nd edn. John Wiley & Sons, New York (1994)
Wang, L., Zhang, Y., Zhang, Y.: On absolute stability for a class of nonlinear control systems with delay. Chin. Sci. Bull. 38(16), 1445–1448 (1993)
Zhang, Y., Heng, P.A., Fu, A.W.C.: Estimate of exponential convergence rate and exponential stability for neural networks. IEEE Trans. Neural Networks 10(6), 1487–1493 (1999)
Zhao, H.: Global asymptotic stability of Hopfield neural network involving distributed delays. Neural Networks 17, 47–53 (2004)
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Marghany, M., Hashim, M., Cracknell, A.P. (2008). Hopfield Neural Network for Sea Surface Current Tracking from Tiungsat-1 Data. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69848-7_75
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DOI: https://doi.org/10.1007/978-3-540-69848-7_75
Publisher Name: Springer, Berlin, Heidelberg
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