Region-Line Association Constraints for High-Resolution Image Segmentation

Abstract

Man-made objects, including buildings and roads, often feature straight boundaries. Specific shape features, including parallel boundaries (e.g., roads and buildings) and perpendicular corners (e.g., buildings), are strong structural clues for distinguishing man-made objects from natural objects. In this study, such observations are implemented in remote-sensing image segmentation. Several region-line association constraints are proposed, including the parallel straight line (PLSL) neighborhood, the perpendicular straight line (PPSL) neighborhood, and the PLSL zone. A region-merging process with these structural constraints is appended to the hard-boundary-constrained segmentation method (HBC-SEG), which is a multiscale image segmentation method. Results show that, compared with HBC-SEG, the proposed method presents a significant decrease in oversegmentation errors (measured by recall ratio r) and an insignificant increase in undersegmentation errors (measured by precision p). As a result, measure m 2 , which synthetically evaluates undersegmentation and oversegmentation errors, increases significantly. The improved method helps obtain complete objects, thus facilitating feature extraction and image classification for object-based image analysis.