Spatial big data dictionary learning method based on particle swarm optimization

Abstract

The invention discloses a spatial big data dictionary learning method based on particle swarm optimization. The spatial big data dictionary learning method includes step 1, preprocessing spatial big data; step 2, learning an online dictionary learning (ODL) dictionary, and utilizing an ODL algorithm to process remotely sensed data which are currently read in and after being separated to acquire a priori dictionary; step 3, building a particle swarm optimization model; step 4, performing PSO through a particle swarm optimization algorithm to acquire a new dictionary after being optimized; step 5, adopting an algorithm stabilizing mechanism to introduce an intermediate variable in the ODL algorithm, and transmitting updating information of a sparse coefficient matrix generated by the new dictionary after PSO; step 8, judging whether data exist in a spatial large-size data set or not. The spatial big data dictionary learning method has the advantages that large-size remotely sensed data are processed effectively, accuracy in spatial big-data expression is improved under the circumstance that calculating load is not increased as much as possible, and noise contained in remotely sensed data in the process of rebuilding can be inhibited well.

Description

technical field [0001] The present invention relates to space-oriented big data dictionary learning technology, in particular, relates to a space big data dictionary learning method based on particle swarm optimization. Background technique [0002] In recent years, with the rapid development of aerospace, aviation and remote sensing data acquisition technology, massive spatial data has been generated. Facing the massive growth of spatial data, traditional data acquisition and analysis methods have been unable to adapt; therefore, data The idea of ​​sparse representation came into being; sparse representation mainly involves two aspects of research: the construction of dictionaries and the method of solving sparse coefficients; in the research field of non-analytic over-complete dictionary construction, the research of these two aspects has a certain coupling. [0003] There are two main methods of dictionary construction: using mathematical tools to generate dictionaries, t...

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Problems solved by technology

Therefore, as far as the proposed algorithm is concerned, the data distribution characteristics of large-scale data are not considered when performing large-scale training, so the training effect is not ideal; therefore, it is still not enough to deal with the training of large spatial data

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