With the advancement of synthetic aperture radar (SAR) sensor technology, linear structures such as building facades have become increasingly discernible in SAR imagery. Accurate detection of these line features is critical for object recognition and 3D model reconstruction. To the best of our knowledge, few existing methods explicitly address the problem of detecting lines composed of point scatterers. In this paper, we analyze the characteristics of such lines and propose a novel point scatterer-driven growth-based approach, termed PSG-Line, for their detection. Point scatterers are first extracted by combining the ordered-statistics constant false alarm rate (OS-CFAR) algorithm with non-maximum suppression and Harris corner response thresholding. Line segments are then initiated from these scatterers and iteratively extended by incorporating subsequent points that satisfy a set of geometric constraints. Finally, the detected line segments are validated based on the Helmholtz principle. Local principal orientations of point scatterers are estimated and incorporated into the line segment growth and validation stages. Both simulation and real-life SAR data experiments demonstrate that the PSG-Line algorithm outperforms existing line detection methods in accurately detecting lines composed of point scatterers.
Zhang et al. (Sun,) studied this question.