This dissertation aims to evaluate segmentation algorithms for remote sensing images. Four segmentation algorithms were considered in this study. These algorithms have different approaches such as clustering-based, region growing-based, bayesian-based and graph-based. As each algorithm has its own parameters, the process to find their optimum values was done using an optimization algorithm, Nelder - Mead. Nelder - Mead algorithm looks for the best parameters for each segmentation algorithm, i.e. the parameters that provide the most accurate results with respect to a given reference. The objective function was defined by seven different metrics. These metrics assess qualitatively the segmentation result based on its reference. The experiments were performed over three remote sensing images from different locations of Brazil. A total of 84 experiments have been performed. The results have shown that graph-based approaches produce the best results based on each metric. The region growing- and clustering-based approaches have shown to be good alternatives for remote sensing images.