In this research paper, we propose supervised and unsupervised change detection methodologies focused on the analysis of multitemporal Synthetic Aperture Radar (SAR) images. These approaches are based on three main steps: (1) a comparison of multitemporal image was carried out by normalized difference ratio (NDR) operator; (2) implementing a novel supervised or unsupervised thresholding and (3) generating the change map by coupling of thresholding along with a region growing algorithm. In the first step, the two filtered multitemporal images were used to generate NDR image that was subjected to analysis. In the second step, by assuming a Gaussian distribution in the nochange area, we identified the pixel range that fits the Gaussian distribution better than any other range iteratively to detect the no-change area that eventually separates the change areas. In the supervised method, several sample no-change pixels were selected and the mean (μ) and the standard deviation (σ) were obtained. Then, μ±3σ was applied to select the best threshold values. Finally, a traditional thresholding algorithm was modified and implemented with the coupling of the region growing algorithm to consider the spatial information to generate the change map. The Gaussian distribution was assumed because it better fits the conditional densities of the no-change class in the NDR image. The effectiveness of the proposed methods was verified with the simulated images and the real images associated to geographical locations. The results were compared with the manual trial and error procedure (MTEP) and traditional unsupervised expectation-maximization (EM) method. Both proposed methods gave similar results with MTEP and significant improvement in Kappa coefficient in comparison to the traditional EM method was found in both cities. The coupling of the modified thresholding with the region growing algorithm is very effective with all methods.
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