Refining High Spatial Resolution Remote Sensing Image Segmentation for Man-made Objects through aCollinear and Ipsilateral Neighborhood Model

Abstract

AbstractMan-made objects, such as buildings and roads, which areimportant targets for information extraction from high spatial resolution (HSR) remote sensing images, often feature straight boundaries. This study employs this knowledge on HSR image segmentation by embedding a straight-line constraint in regionbased image segmentation. A new concept called collinear and ipsilateral neighborhood is proposed and applied to hardboundary constraint-based image segmentation for accuracy improvement. In the experimental areas, the method accuracy measured by recall ratio r increases from 0.036 to 0.048 (on the average) after the refinement, with significantly smaller decreases in precision p that are all less than 0.006. In sum, the proposed technique effectively reduces over-segmentation errors and maintains the same level of under-segmentation error ratio, particularly in man-made areas. It facilitates subsequent objectbased image analyses, including feature extraction, object recognition, and classification.