A hybrid PSO/ACO algorithm for land cover classification

For several decades the remote sensing image classification methods for depicting land cover have gained a great achievements, but with the more multi-source and multidimensional data, the conventional remote sensing image classification methods based on statistical theory have exposed some limitation. So in recent years, artificial intelligence techniques have being applied to remote sensing image classification, the purpose of which is to reduce the undesired limitations of the conventional classification methods. Ant colony optimization (ACO) and Particle swarm optimization (PSO) as the two main algorithms of swarm intelligence, and because of the self-organization, cooperation, communication and other intelligent merits, they have great potential in remote sensing image processing. This paper introduces remotely sensed image classification using the hybrid ACO/PSO algorithm. The experiment results show that ACO/PSO algorithm has provided a new method for remote sensing image classification.