Abstract
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.
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
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)
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)
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)
Yonghong, J.: Fusion of Landsat TM and SAR image based on principal component analysis. Remote Sensing Technology and Application 13(3), 46–49 (1998)
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)
Michie, D.S., Taylor, D.: Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
Oliver, C.J., Quegan, S.: Understanding SAR Images. Artech House, Boston (1998)
Chen, K.M., Chen, S.Y.: Color Texture segmentation using feature distributions. Pattern Recognition Letters 23, 755–771 (2002)
Mao, K.Z.: RBF Neural Network Center Selection Based on Fisher Ratio Class Separability Measure. IEEE trans. Neural Networks 13, 1211–1217 (2002)
Chen, Y.Q., Nixon, M.S., Thomas, D.W.: Statistical Geometrical Features for Texture Classification. Pattern Recognition 28(4), 537–552 (1995)
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)
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)
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)
Schwenker, F., Kestler, H.A., Palm, G.: Three learning phases for radial-basis-function networks. Neural Networks 14, 439–458 (2001)
Francesco, L., Marco, S.: Effcient training of RBF neural networks for pattern recognition. IEEE trans. on neural networks 12(5), 1235–1241 (2001)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y.W., Li, B.Y. (2006). Remote Sensing Image Fusion Based on Adaptive RBF Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_35
Download citation
DOI: https://doi.org/10.1007/11893257_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)