HIGH-RATIO COMPRESSION OF REMOTE SENSING IMAGE BASED ON RIDGELET AND NEURAL NETWORK
基于脊波和神经网络的大压缩比遥感图像压缩

To get a high-ratio compression of remote sensing images,a neural network(NN)-based compression method was advanced.By using the characteristics of self-learning,parallel processing and distributed storage of NN,a single hidden layer feed-forward NN was constructed for getting high-ratio compression of remote sensing images.Moreover,we employ ridgelet,which is a new geometrical multiscale analysis(GMA) tool and is powerful in dealing with linear singularities(and curvilinear singularities with a localized version),as the activation function in the hidden layer of the network.Therefore the network has both the advantages of NN-based image compression method and more effective representation of edges and contours for the localization properties of ridgelet in scale,location and direction.The simulation results show that the proposed network can not only get high compression ratio but also present promising results,such as high reconstruction quality,fast learning and robustness,as compared to available techniques in the literature.