Multifrequency PolSAR Fusion Method Based on Scattering Mechanism

Effectively fusing multifrequency polarimetric synthetic aperture radar (PolSAR) data has emerged as a key research focus, as it holds significant potential for optimizing the interpretation of diverse scattering mechanisms in complex environments. However, current fusion technologies primarily rely on data-driven machine learning or statistical models. While these methods effectively achieve multifrequency data fusion, they often lack clear physical interpretation, making it challenging to directly explain or quantify the contributions of different frequencies. To address these challenges, a general fusion method is proposed in this letter, which introduces five canonical scattering models to guide the fusion of multifrequency data. This method fully considers the scattering diversity within individual pixels while ensuring the physical significance of the fusion process. Specifically, we systematically investigate various combinations of multifrequency data and apply the sequential quadratic programming (SQP) algorithm to optimize the similarity between the fused data and the canonical scattering models. The combination that maximizes this similarity is identified as the optimal multifrequency data fusion configuration. The RADARSAT-2 C-band full-polarized data and the ALOS-2 L-band full-polarized data acquired over the San Francisco area are applied to validate the proposed method. The experimental results demonstrate that the fused image offers superior performance in interpreting ground features compared to the original images.