This work investigates a statistical technique for high performance remote-sensing imagery compression. By exploiting existing remote-sensing data sets, useful structural and texture prior information can be learned. The main methodologies are Bayesian dictionary learning and stochastic approximation. A Bayesian network simulating the generation mechanism of remote- sensing images is modelled. The whole compression scheme is established. And the corresponding inference algorithm using Gibbs sampling is given, where the inference is realized in an online way. The performance of the proposed compressing scheme is evaluated over a high-resolution remote-sensing image data set captured by TH-1 series satellites. Experiment results have shown that our compression scheme outperforms JPEG-2000 by 3dB on average with same bits-per-pixel performance, and that Bayesian learning can provide a dictionary with high expressiveness for remote-sensing images. In addition, with online learning skills our proposed compression scheme can scale up to very large-scale training data.


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    Title :

    Online Bayesian Learning for Remote-Sensing Imagery Compression


    Contributors:
    Zhang, Zizhuo (author) / Li, Shaoyang (author) / Tao, Xiaoming (author) / Dong, Linhao (author) / Lu, Jianhua (author)


    Publication date :

    2017-06-01


    Size :

    500940 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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