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BI Hui, ZHONG Yuan, JIN Shuang, et al. Automated Registration of SAR Imagery and GIS Building Footprints Based on Morphological Indices[J]. Journal of Radars, in press. doi: 10.12000/JR26119
Citation: BI Hui, ZHONG Yuan, JIN Shuang, et al. Automated Registration of SAR Imagery and GIS Building Footprints Based on Morphological Indices[J]. Journal of Radars, in press. doi: 10.12000/JR26119
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Automated Registration of SAR Imagery and GIS Building Footprints Based on Morphological Indices

DOI: 10.12000/JR26119 CSTR: 32380.14.JR26119
  • 1.

    College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

  • 2.

    Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing 211106, China

Funds:  The National Natural Science Foundation of China (62271248)
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  • Corresponding author: BI Hui, bihui@nuaa.edu.cn
  • Received Date: 2026-06-29
  • Rev Recd Date: 2026-08-28
    • Available Online: 2026-09-01
    Abstract
  • To effectively address the automated registration of high-resolution Synthetic Aperture Radar (SAR) imagery and Geographic Information System (GIS) building footprints in complex urban environments, this paper proposes an automated SAR-GIS registration framework based on morphological indices. The proposed framework consists primarily of two stages: Feature extraction and registration. In the feature extraction stage, multiscale morphological Double-bounce Index (DI) are first used to generate initial building masks. Subsequently, height distribution density derived from SAR Tomography (TomoSAR) is incorporated as prior information to establish a spatial consistency filtering criterion for mask refinement. Finally, the visible building base edges, which exhibit regular geometric characteristics, are extracted and further refined using building layout statistics and directional constraints to obtain grouped line segment features representing SAR building signatures. In the registration stage, a direction-constrained, grid-based iterative registration method is developed. By leveraging the previously extracted building layout as prior information, directional constraints are applied to the visible edges of GIS footprints to achieve global orientation correction. Furthermore, a grid-based local iterative strategy is introduced to construct a compensation mechanism for nonlinear geometric distortions, thereby enabling high-precision SAR-GIS building footprint registration. Experimental validation across multiple scenarios using Fucheng-1 satellite data demonstrates that the proposed framework achieves more than a 12% improvement in building feature extraction accuracy compared with traditional Potts segmentation and morphological baseline algorithms. Furthermore, compared with the traditional Iterative Closest Point (ICP) algorithm, the proposed framework improves registration distance accuracy by more than 46%, while maintaining a registration success rate above 88%. These results indicate that the proposed approach effectively addresses the limitations of low efficiency and poor robustness associated with traditional rigid registration algorithms in complex urban environments.

     

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