Segmentation, the first step of object-based classification, is crucial to the quality of the final classification results. A poor quality of the segmentation leads directly to a low quality of the classification. Therefore, it is very important to evaluate the segmentation results using quantitative methods and to know how to obtain the best results. To obtain the best possible segmentation results, it is important to choose the right input data resolution as well as the best algorithm and its parameters for a specific remote sensing application. The impact of the segmentation algorithm, the parameter settings, as well as the spatial and spectral resolution of the data is investigated. To describe these impacts, we performed more than 70 segmentations of a Worldview-2 image. The impact of the spectral resolution was tested with 10 combinations of data on different spectral channels, and the impact of the spatial resolution was tested on an original and downsampled test image to four different spatial resolutions. We investigated these impacts on the segmentation of objects that belong to the classes urban, forest, bare soil, vegetation, and water. The impacts on the segmentation are described using a common methodology for the evaluation of segmentation.