Segmentation of High Spatial Resolution Remote Sensing Imagery Based on Hard-Boundary Constraint and Two-Stage Merging

This paper proposes a novel two-stage method for remote sensing image segmentation. First, initial small segments, also called subobject primitives (sub-OPs), are obtained using edge-constrained watershed segmentation and edge allocation. These segments are gradually merged into a larger segment until the edge-controlled limits are reached, thereby creating the initial OPs. In this stage, a concept of hard-boundary ratio is proposed to control the merge effectively. Second, nonconstrained merging is conducted on the OPs, which results in final segmentation. In addition, a repeatable pairwise segment-merging scheme is utilized. This scheme improves method efficiency and accuracy. Comprehensive experiments comparing this new method with the multiresolution segmentation method of eCognition were conducted. Results show that this new method has the following advantages: 1) higher segmentation accuracy and OP boundary precision and 2) less dependence on the scale parameter.