Automatic Target Detection in High-Resolution Remote Sensing Images Using a Contour-Based Spatial Model

In this letter, we propose a contour-based spatial model which can detect geospatial targets accurately in high-resolution remote sensing images. To detect the geospatial targets with complex structures, each image was partitioned into pieces as target candidate regions using multiple segmentations at first. Then, the automatic identification of target seed regions is achieved by computing the similarity of the contour information with the target template using dynamic programming. Finally, the contour-based similarity was further updated and combined with spatial relationships to figure out the missing parts. In this way, a more accurate target detection result can be achieved. The precision, robustness, and effectiveness of the proposed method were demonstrated by the experimental results.