We present a novel systematic method for change detection in dual polarimetric (Dual-pol) synthetic aperture radar (SAR) images based on swarm intelligence techniques and fractal geometry. As the two main algorithms of swarm intelligence, ant colony optimization (ACO) and particle swarm optimization (PSO) have great potential in change detection. Additionally, fractal geometry appears to be a highly effective means of characterizing textural features in Dual-pol SAR images. The proposed method exploits fractal images to form a new difference image. Fractal images are computed based on wavelet multiresolution analysis. Moreover, by minimizing an optimal function value in the iteration process, the changes are detected by applying ACO and PSO to the difference image. Experimental results of detecting changes in Dual-pol SAR images reveal that the proposed method is a highly effective and efficient means of change detection in Dual-pol SAR images.
Conventional raster-based cellular automata (CA) confront many difficulties because of cell size and neighborhood sensitivity. Alternatively, vector CA-based models are very complex and difficult to implement. We present a hybrid cellular automata (HCA) model as a combination of cellular structure and vector concept. The space is still defined by a set of cells, but rasterized spatial objects are also utilized in the structure of transition rules. Particle swarm optimization (PSO) is also used to calculate the urbanization probability of cells based on their distance from the development parameters. The proposed model is applied to Landsat satellite imagery of the city of Tehran, Iran with 28.5-m spatial resolution to simulate the urban growth from 1988 to 2010. Statistical comparison of the ground truth and the simulated image using a kappa coefficient shows an accuracy of 83.42% in comparison to the 81.13% accuracy for the conventional Geo-CA model. Moreover, decreasing the spatial resolution by a factor of one-fourth has reduced the accuracy of the HCA and Geo-CA models by 1.19% and 3.04%, respectively, which shows the lower scale sensitivity of the proposed model. The HCA model is developed to have the simplicity of cellular structure together with optimum features of vector models.