Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
A genetic approach to color image compression
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
Weighted centroid neural network for edge preserving image compression
IEEE Transactions on Neural Networks
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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.