Image segmentation is a key step in Object-Based Image Analysis (OBIA) for High Spatial Resolution Remote Sensing Imagery (HSRI). Previous segmentation method used image spectrum and structure information only, so that it is difficult to obtain good segmentation results. A novel segmentation method based on multiple feature fusion and multi-scale region merging was proposed in this paper. Firstly, initial over-segmentation objects were obtained by marked watershed transform. Then, the spectrum, texture, shape, area, and edge intensity information were combined to build the merging cost of adjacent objects, and all objects were mapped to adjacent graph nodes with their merging cost. Finally, the nodes in Region Adjacency Graph (RGA) were merged to get multi-scale segmentation result. Experiment results indicate that the proposed method can produce nested multi-scale segmentation results with accurate boundaries, and achieve a better performance compared to Mean-shift and ENVI segmentation method. © 2016 Taylor & Francis Group, London.