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.
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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
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DOI: https://doi.org/10.1007/11427445_116
Publisher Name: Springer, Berlin, Heidelberg
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