This letter presents a novel spatially-constrained color–texture model for hierarchical segmentation of very high resolution images. The segmentation starts with an initial partition, where the image is partitioned into many homogeneous regions. Then, the regions are regarded as node sets of a region adjacency graph, in which the distances of each pair of adjacent regions are calculated combining color and textural features with spatial constraint. Finally, a stepwise optimized region merging process is applied to obtain hierarchical segmentation results. Experiments and comparisons by using different satellite images are carried out to demonstrate the encouraging performance as well as the high efficiency of the proposed method.