DICTIONARY LEARNING FOR LARGE-SCALE REMOTE SENSING IMAGE BASED ON PARTICLE SWARM OPTIMIZATION

Dictionary learning which is based on the sparse coding has been frequently employed to many tasks related to remote sensing images such as classification, reconstruction and change detection. Recently, many new dictionary learning algorithms which are on an non-analytic dictionary had been proposed. Online Dictionary Learning is the famous one which can be applied to process large-scale images. But the accuracy is decreased for the strategy of updating all atoms at once. So we try to propose our approach based on the improvements of ODL algorithm. In the iterations, we reasonably select special atoms within the dictionary and then introduce the particle swarm optimization into the atom updating stage of the dictionary learning model. Experiments show that our proposed algorithm improves the performance of ODL algorithm on the accuracy of reconstruction for large-scale remote sensing images. And our method has a better effect on noise suppression.