Application of particle swarm optimization to similar image search on satellite sensor data

Remote sensing using satellite monitored sensor data is one of the most important methods for global environmental monitoring. Similar image search is an important problem in the satellite image data analysis. The similar image search extracts local area images from a given global image. The similar image search using shape features can be used to analyze the cloud type, the volcanic activity, the change in vegetation and etc. However, the similar image search in satellite image data requires the fast computation infrastructure and search method due to the huge image data. In previous research, we proposed a variant of particle swarm optimization that globally searches using particle groups in dynamically changed problem space. In this paper, our PSO was applied to the similar image search problem based on Transfer learning concept. The transfer learning is a meta learning methods that uses the knowledge and data in an domain in order to solve the problem in the another domain. In this experiment, we compared the accuracy and the calculation time among the different transfer learning conditions in order to investigate the possibility of knowledge transfer in similar image search.