Remote Sensing Image Fusion Based on Adaptive RBF Neural Network

  • Yun Wen Chen
  • Bo Yu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


With the availability of multi-sensor and multi-frequency image data from operational observation satellites, the fusion of image data has become an important tool in remote sensing image evaluation and segmentation. This paper presents a novel Radius Basis Function (RBF) neural network with some distinctive training strategies, which can integrate multiple information sources efficiently and exploit the potential advantages of each feature. Multi-scale features extracted from remote sensing images are evaluated adaptively and used for segmentation. Experimental results obtained on artificial and real data are both presented which demonstrate the effectiveness of our proposal.


Image Fusion Synthetic Aperture Radar Synthetic Aperture Radar Image Hide Unit Feature Weight 
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  1. 1.
    Pohl, C., Van Genderen, J.L.: Review article multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing 19(5), 823–854 (1998)CrossRefGoogle Scholar
  2. 2.
    Jolly, M., Gupta, A.: Color and texture fusion: application to aerial image segmentation and GIS updating. Image and Vision Computing 18(10), 823–832 (2000)CrossRefGoogle Scholar
  3. 3.
    Carper, J.W., Lillesand, T.M., Kjefer, R.W.: The use of intensity-hue-saturation transformation for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing 56(4), 459–467 (1990)Google Scholar
  4. 4.
    Yonghong, J.: Fusion of Landsat TM and SAR image based on principal component analysis. Remote Sensing Technology and Application 13(3), 46–49 (1998)Google Scholar
  5. 5.
    Aiazzi, B., Alparone, L., Garzelli, A.: Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. Transactions on Geoscience and Remote sensing 40, 2300–2312 (2002)CrossRefGoogle Scholar
  6. 6.
    Michie, D.S., Taylor, D.: Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)MATHGoogle Scholar
  7. 7.
    Oliver, C.J., Quegan, S.: Understanding SAR Images. Artech House, Boston (1998)Google Scholar
  8. 8.
    Chen, K.M., Chen, S.Y.: Color Texture segmentation using feature distributions. Pattern Recognition Letters 23, 755–771 (2002)MATHCrossRefGoogle Scholar
  9. 9.
    Mao, K.Z.: RBF Neural Network Center Selection Based on Fisher Ratio Class Separability Measure. IEEE trans. Neural Networks 13, 1211–1217 (2002)CrossRefGoogle Scholar
  10. 10.
    Chen, Y.Q., Nixon, M.S., Thomas, D.W.: Statistical Geometrical Features for Texture Classification. Pattern Recognition 28(4), 537–552 (1995)CrossRefGoogle Scholar
  11. 11.
    James, J.S., Mcintire, T.J.: A recurrent neural network classifier for improved retrievals of areal extent of snow cover. IEEE trans. on Geosciene and remote sensing 39(10), 2135–2147 (2001)CrossRefGoogle Scholar
  12. 12.
    Singh, M., Singh, S.: Spatial texture analysis: a comparative study. In: Proc. 15th International Conf. on Pattern Recognition (ICPR 2002), vol. 1, pp. 676–679 (2002)Google Scholar
  13. 13.
    Sami, M.A., Jones, W.L., Park, J.D., Ferguson, S.M.: A neural network algorithm for sea ice edge classification. IEEE trans. on Geosciene and remote sensing 35(4), 817–826 (1997)CrossRefGoogle Scholar
  14. 14.
    Schwenker, F., Kestler, H.A., Palm, G.: Three learning phases for radial-basis-function networks. Neural Networks 14, 439–458 (2001)CrossRefGoogle Scholar
  15. 15.
    Francesco, L., Marco, S.: Effcient training of RBF neural networks for pattern recognition. IEEE trans. on neural networks 12(5), 1235–1241 (2001)CrossRefGoogle Scholar
  16. 16.
    Wettschereck, D., Dietterich, T.: Improving the performance of Radial Basis Function networks by learning center locations. Advance in Neural Information Processing System, vol. 4. Morgan Kaufmann Publisher, San Francisco (1992)Google Scholar
  17. 17.
    Chitra, P., Marimuthu, P.: Daniel Ralph, Chris Manzie: Effects of moving the centers in an RBF network. IEEE trans. on Neural Networks 13(6), 1299–1307 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yun Wen Chen
    • 1
  • Bo Yu Li
    • 1
  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringFudan UniversityShanghaiChina

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