A Region-Line Primitive Association Framework for Object-Based Remote Sensing Image Analysis

In this study, we propose a novel region-line primitive association framework (RLPAF) for OBIA. In this framework, segments (region primitive) and straight lines (line primitive) are obtained by image segmentation and straight line detection, respectively, before their corresponding intra-primitive features are extracted. An association model is built on inter-primitive topology and direction relationships. Several region-line collaborative features are also derived. Image analysis is then performed based on both region and line primitives. The advantage of RLPAF is the collaborative utilization of complementary information between regions and lines throughout the entire OBIA process: from image segmentation, to feature extraction, and finally, object recognition. To validate this framework, RLPAF is applied on road network extraction from high spatial resolution (HSR) remote sensing images. Experiments show that the proposed framework and methods refine primitive shape and spatial relationship analyses, as well as obtain higher method accuracy, than OBIAs based on only regions.