A Polarimetric Information-Driven 3-D Imaging Framework for Complex Urban Scenes

To address the critical challenge of simultaneous interpretation for hybrid scattering targets in complex urban environments, this article proposes a novel polarimetric information-based 3-D imaging framework. By integrating polarimetric decomposition with morphological operations, this framework achieves preliminary classification between dense man-made regions and natural terrains. For the layover of man-made targets, we propose a polarization-based joint sparse method. It can prejudge the dominant scatterer within pixel blocks and selectively extract optimal polarization channels to facilitate layover separation by leveraging differences across various polarization channels. For distributed natural terrains, we employ a spectral analysis method based on polarimetric covariance matrix (CM) estimation. It extracts nonlocal homogeneous pixel blocks within the polarization-Euclidean space through the synergistic integration of scattering mechanism measurements and statistical elevation priors, thereby enabling CM and elevation estimation. Subsequently, from both simulated and real-data experiments, the proposed framework has been validated as effective in enhancing reconstruction accuracy and completeness through optimal utilization of polarization information.

• Publication year

  2025

• Venue

  IEEE Transactions on Geoscience and Remote Sensing

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/TGRS.2025.3624557
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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