Edge-Guided Image Object Detection in Multiscale Segmentation for High-Resolution Remotely Sensed Imagery

A new segmentation approach for high-resolution remotely sensed imagery that combines the global edge and region information is developed from a new scheme to monitor the best conditions for each growing object to obtain the corresponding meaningful image object during multiscale analysis. The approach, which is an extension of the image object detection approach, includes new algorithms for determination of region-growing criteria, edge-guided image object detection, and assessment of edges. The method consists of two stages: In the first stage, edges are acquired from edge detection with embedded confidence and stored in an R-tree, and initial objects are segmented by eCognition and organized in the region adjacency graph; in the second stage, meaningful image objects are obtained by incorporating multiscale segmentation and analyzing the edge completeness curve. The evaluation results of edge completeness are obtained within the process of multiscale segmentation, and the assessment for the segmentation results shows its merit in coastal remote sensing. Images containing plenty of weak edges or distributing scene objects with various sizes and shapes can fully embody the strength of this method.