Abstract
In this paper, a combination of artificial neural network (ANN)and genetic algorithm(GA) has been proposed as a method to obtain a high accuracy in classification of polarimetric SAR data. First we extracted 57 features based on decomposition algorithms and then the best features among inputted features by use of GA-ANN wereselected.The classification results of a data set, composed of different land cover elements, exhibited higher accuracy than maximum likelihood and Wishart classifier; moreover the input features were decreased to small numbers which contain sufficient information for classification of data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Richards, J.A., Jia, X.: Remote sensing digital image analysis an introduction, ch. 1, 4th edn. Springer, Heidelberg (2006)
Lee, J.S., Pottier, E.: Polarimetric radar imaging, pp. 1–28, 53–85, 179– 224. CRC Press, Boca Raton (2009)
Lee, J.S., Grunes, M.R., Ainsworth, T.L., Du, L.J., Schuler, D.L.: Unsupervised classification using polarimetric decomposition and the complex wishart classifier. IEEE Trans. Geosci. Remote Sens. 37(5), 2249–2258 (1999)
Kim, Y., Van Zyl, J.J.: On the relationship between polarimetric parameters. In: Proceedings of IEEE 2000 International Geoscience and Remote Sensing Symposium, IGARSS 2000, vol. 3, pp. 1298–1300 (2000a)
Kim, Y., Van Zyl, J.J.: Overview of polarimetric interferometry. In: IEEE Aerospace Conference Proceedings, vol. 3, pp. 231–236 (2000b)
Burini, A., Putignano, C., Del Frate, F., Del Greco, M., Schiavon, G., Solimini, D.: A neural approach to unsupervised classification of very-high resolution polarimetric SAR data. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 4164–4166 (2007)
Cloude, S.R., Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR.IEEE Trans. Geosci. Remote Sens. 35(1) (1997)
Famil, L.F., Pottier, E., Lee, J.S.: Unsupervised classification of multi-frequency and fully polarimetric SAR images based on the H/A/Alpha–wishart classifier. IEEE Trans. Geosci. Remote Sens. 39(11), 2332–2342 (2001)
Chang, G., Oh, Y.: Polarimetric SAR image classification based on the degree of polarization and co-polarized phase-difference statistics. In: Proceedings of Asia-Pacific Microwave Conf., APMC 2007, pp. 1–4 (2007)
Ersahin, K., Scheuchl, B., Cumming, I.: Incorporating Texture Information into Polarimetric Radar Classification Using Neural Networks. In: Geosc. Remote Sens. Symposium, IGARSS 2004, vol. 1, pp. 560–563 (2004)
Taner, M.T., Images, R.S.: kohonen’sself-organizing networks with “conscience”, Kohonen’sself-organizing maps and their use in interpolation (1997)
Hara, Y., Atkins, R.G., Shin, R.T., Kong, J.A., Yueh, S.H., Kwok, R.: Application of neural networks for sea ice classification in polarimetric SAR images, radar image classification. IEEE Trans. Geosci. Remote Sens. 33(3), 740–748 (1995)
Baraldi, A., Parmiggiani, F.: A neural network for unsupervised categorization of multi valued input patterns: an application to satellite image clustering. IEEE Trans. Geosci. Remote Sens. 33(2), 305–316 (1995)
Shah, S.K., Gandhi, V.: Image classification based on textural features using artificial neural network (ANN). IE (I) Journal-ET 84 (2004)
Van Coillie, F.M.B., Verbeke, L.P.C., Wulf, R.R.D.: 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)
Krose, B., Smagt, P.V.D.: An introduction to neural networks, 8th edn. University of Amsterdam (1996)
Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haddadi G., A., Sahebi, M. (2011). Combination of GA and ANN to High Accuracy of Polarimetric SAR Data Classification. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-21501-8_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21500-1
Online ISBN: 978-3-642-21501-8
eBook Packages: Computer ScienceComputer Science (R0)