The popular multiresolution segmentation (MRS) algorithm is time and memory consuming when dealing with large images because it uses the pixel-grid for the initial object representation. In this study, we have tested a new workflow for image segmentation of remote sensing data, starting the MRS (using the ESP2 tool) from the superpixel level (using SLIC superpixels) and aiming at dramatically reducing the amount of time and computational resources needed to automatically partition relatively large datasets of very high resolution (VHR) remote sensing images. Tests were done on Quickbird and WorldView-2 data and the results show that the proposed workflow outperforms the traditional approach (MRS starting from pixels). The computational time was reduced in all cases, the biggest improvement being from 5h 35min to 13 min, for a WorldView-2 scene with 8 bands and an extent of 12.2 million pixels. This also comes with a slight improvement of the geometric accuracy of the extracted objects. This approach has the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint.