Particle Swarm Optimization based dictionary learning for remote sensing big data
Dictionary learning, which is based on sparse coding, has been frequently applied to many tasks related to remote sensing processes. Recently, many new non-analytic dictionary-learning algorithms have been proposed. Some are based on online learning. In online learning, data can be sequentially incorporated into the computation process. Therefore, these algorithms can train dictionaries using large-scale remote sensing images. However, their accuracy is decreased for two reasons. On one hand, it is a strategy of updating all atoms at once; on the other, the direction of optimization, such as the gradient, is not well estimated because of the complexity of the data and the model. In this paper, we propose a method of improved online dictionary learning based on Particle Swarm Optimization (PSO). In our iterations, we reasonably selected special atoms within the dictionary and then introduced the PSO into the atom-updating stage of the dictionary-learning model. Furthermore, to guide the direction of the optimization, the prior reference data were introduced into the PSO model. As a result, the movement dimension of the particles is reasonably limited and the accuracy and effectiveness of the dictionary are promoted, but without heavy computational burdens. Experiments confirm that our proposed algorithm improves the performance of the algorithm for large-scale remote sensing images, and our method also has a better effect on noise suppression.
particle swarm optimization big data online dictionary learning sparse representation machine learning