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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...
Requirements for higher resolution remote sensing images lead to rapid increase of data amount in space communications. However, since satellite communications capacity is suffering from great pressure, seeking for more effective compression scheme is supposed to solve existing conflict between tremendous data and limited bandwidth. For this reason, this paper proposes a prior-information-based remote...
The degree of abundance of labeled training data is an important factor in determining the performance of supervised machine learning systems. However, in some applications, labeled data are either costly to collect or easily outdated, resulting in poor generalization of trained machine learners. Nonetheless, there are often related domains where large corpuses of labeled data can be easily obtained...
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