Efficient segmentation of remote sensing images needs optimally estimated parameters for any segmentation algorithm. These optimal parameters help algorithms avoid both over- and under- segmentation of image data and provide high-quality inputs for further processing.Recently, the super-pixels method has been introduced as a powerful tool to over-segment the images and replace the pixels with higher-level inputs. Automatic aggregation of super-pixels with image segments is a challenge in the remote sensing and computer programming community. In this paper, a new automated segmentation method, namely density-based super-pixel aggregation (DBSPA), is proposed. This method is based on the spatial clustering algorithm for integrating the obtained super-pixels from the Simple Linear Iterative Clustering (SLIC). The DBSPA algorithm uses a Normalized Difference Vegetation Index (NDVI) and a normalized Digital Surface Model (nDSM) to form core segments and defines the primary structure of geographic features in an image scene. Then, the box-whisker plot was used to analyze the statistical similarity of super-pixels to each core-segment, and spatially cluster all super-pixels. In our experiments, two ultra-high-resolution datasets selected from ISPRS semantic labelling challenge were used. As for the Vaihingen dataset, the overall accuracy was 83.7%, 84.8%, and 89.6% for pixel-based, object-based, and the proposed method respectively. The values for the Potsdam dataset are 85.2%, 85.6%, and 86.4%. The evaluation of results revealed an overall accuracy improvement in Random Forest classification results, while the number of image objects reduced by about 4%.