A Multiple Remote Sensing Sensor Fusion System Using Choquet Fuzzy Integral and Modified Particle Swarm Optimization (FI-MPSO)

Abstract

The simultaneous use of remote sensing sensors has been of great interest due to reasons such as the weaknesses of each sensor individually and the improvement in results by integrating multisensor information. In this context, decision fusion of multiremote sensing sensors is one of the hot research topics for improvement in land cover classification. This paper investigates a multisensor fusion system based on Choquet fuzzy integral and a modified particle swarm optimization for fusion of high-resolution visible RGB image and thermal infrared hyperspectral. Instead of using common optimization algorithms, a new version of PSO is used to reduce the running time and to increase the accuracy. After some processes and fuzzy classifications, a fuzzy integral–fuzzy measure fusion methodology which uses modified PSO to find the best subset of fuzzy measures is utilized to fuse the resulted decision profiles of soft decision-making system. Also, comparison between proposed method and other successful decision fusion techniques such as Dempsters shafer, AdaBoost and Bagging is investigated. Experiments are executed on high-resolution RGB image and thermal infrared hyperspectral data from Quebec of Canada. The results show that the suggested method obtains 92% of overall accuracy and outperforms the other decision fusion and optimization methods in classification performance.