Unsupervised Change Detection in Multitemporal Multispectral Satellite Images Using Parallel Particle Swarm Optimization

In this paper, a novel algorithm for unsupervised change detection in multitemporal multispectral images of the same scene using parallel binary particle swarm optimization (PBPSO) is proposed. The algorithm operates on a difference image, which is created by using a novel fusion algorithm on multitemporal multispectral images, by iteratively minimizing a cost function with PBPSO to produce a final binary change-detection mask representing changed and unchanged pixels. Each BPSO of parallel instances is run on a separate processor and initialized with a different starting population representing a set of change-detection masks. A communication strategy is applied to transmit data in between BPSOs running in parallel. The algorithm takes the full advantage of parallel processing to improve both the convergence rate and detection performance. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on semisynthetic and real-world data sets. The semisynthetic results for different levels of Gaussian noise are obtained in terms of false and miss alarm (MA) rates between the estimated change-detection mask and the ground truth image. The proposed method on the semisynthetic data with high level of Gaussian noise obtains the final change-detection mask with a false error rate of 1.50 and MA error rate of 14.51.