Swarm intelligence for classification of remote sensing data

This paper proposes a new method to classify remote sensing data by using Particle Swarm Optimization (PSO). This method is to generate classification rules through simulating the behaviors of bird flocking. Optimized intervals of each band are found by particles in multi-dimension space, linked with land use types for forming classification rules. Compared with other rule induction techniques (e.g. See5.0), PSO can efficiently find optimized cut points of each band, and have good convergence in the search process. This method has been applied to the classification of remote sensing data in Panyu district of Guangzhou with satisfactory results. It can produce higher accuracy in the classification than the See5.0 decision tree model.

This paper proposes a new method to classify remote sensing data by using Particle Swarm Optimization (PSO). This method is to generate classification rules through simulating the behaviors of bird flocking. Optimized intervals of each band are found by p