Abstract
To get a high-ratio compression of remote sensing images, we advanced a new compression method using neural network (NN) and a geometrical multiscale analysis (GMA) tool-ridgelet. Ridgelet is powerful in dealing with linear singularity (or curvilinear singularity with a localized version), so it can represent the edges of images more efficiently. Thus a network for remote sensing image compression is constructed by taking ridgelet as the activation function of hidden layer in a standard three-layer feed-forward NN. Using the characteristics of self-learning, parallel processing, and distributed storage of NN, we get high-ratio compression with satisfying result. Experiment results indicate that the proposed network not only outperforms the classical multilayer perceptron, but also is quite competitive on training of time.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Manual of Aerial Survey: Primary Data Acquisition. Roger Read (2004)
Netravali, A.N., Limb, J.O.: Picture Coding: A Review. Proc. IEEE 68, 366–406 (1980)
Robert, D.D.: Neural Network Approaches to Image Compression. Processing of IEEE 83, 288–303 (1995)
Feiel, H.: A Genetic Approach to Color Image Compression, Symposium on Applied Computing. In: Proceedings of the ACM symposium on Applied computing, pp. 252–256 (1997)
Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)
Kohno, R., Arai, M., Imai, H.: Image Compression Using a Neural Network with Learning Capability of Variable Function of a Neural unit. In: SPIE Visual Communications and Image Processing 1990, vol. 1360, pp. 69–75 (1990)
Kung, S.Y.: Adaptive Principal Component Extraction (APEX) and Applications. IEEE Transactions on Signal Processing 42, 1202–1217 (1994)
Krishnamurthy, A.K., Ahalt, S.C., Melton, D.E., Chen, P.: Neural Networks for Vector Quantization of Speech and Images. IEEE J. on Selected Areas in Communications 8, 1449–1457 (1990)
Dianat, S.A., Nasrabadi, N.M., Venkataraman, S.: A Non-linear Predictor for Differential Pulse-code Encoder (DPCM) Using Artificial Neural Networks. In: Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Toronto, Canada, pp. 2793–2796 (1991)
Hatami, S., Yazdanpanah, M.J., Forozandeh, B., Fatemi, O.A.: Modified Method for codebook Design with Neural Network in VQ Based Image Compression Circuits and Systems. In: ISCAS, vol. 2, pp. 612–615 (2003)
De Almeida, F.W.T.: A Neural and Morphological Method for Wavelet-based Image- Compression Neural Networks. In: IJCNN, vol. 3, pp. 2168–2173 (2002)
Park, D.: Weighted Centroid Neural Network for Edge preserving Image Compression Neural Networks. IEEE Transactions neural network, 1134–1146 (2001)
Centroid.: Neural Network for Unsupervised Competitive Learning Dong-Chul Park. IEEE Transactions on Neural Networks, 1045–9227 (2000)
Cottrell, G.W.: Principal Components Analysis of Images via Back propagation. SPIE Visual Communications and Image Processing 1001, 1070–1077 (1988)
Yang, G., Tu, X.: A high Efficiency Image Data Compression Scheme based on Wavelet and Neural Network. Opto-Electronic Engineering 31 (2004)
Candes, E. J., Ridgelet: Theory and Applications. Ph.D. dissertation. Stanford Univ. (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, S., Wang, M., Jiao, L. (2005). Compression of Remote Sensing Images Based on Ridgelet and Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_116
Download citation
DOI: https://doi.org/10.1007/11427445_116
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)