An Adaptive Regular Window Optimization-Based Radiometric Calibration Model for Airborne SAR

The radiometric calibration is critical for ensuring the quantitative reliability of synthetic aperture radar (SAR) sensor across multiple applications. However, traditional calibration models often struggle with adaptability when corner reflectors (CRs) deviate from ideal cross-shaped responses and appear instead as patch-like bright spots, thereby reducing calibration accuracy. This paper proposed a core response energy extraction model for CRs based on adaptive regular window (ARW) optimization, leading to the development of an improved SAR radiometric calibration model, referred as ARW-RC. The ARW RC significantly improves the completeness of core energy extraction, background clutter suppression, and adaptability. The core energy extraction of CRs from multi-band SAR images in Hainan and Rizhao demonstrates that the proposed model effectively captures the core region boundaries, proving its robustness and adaptability across diverse imaging scenarios. Specifically, compared with traditional calibration models, the ARW-RC achieved a standard deviation of 0.55 dB for the CRs response energy in X-band SAR image. After radiometric calibration, the relative accuracy improved to 0.70 dB, representing more than a twofold improvement in radiometric accuracy over traditional models. Additionally, the absolute accuracy improved to 0.50 dB, an improvement of 0.69 dB. For the S-band SAR image, the ARW-RC achieved a standard deviation of 1.37 dB in CRs response energy. The relative and absolute accuracy were 1.52 dB and 1.14 dB, respectively. These confirm that the ARW-RC model offers high accuracy and broad applicability, providing an effective solution for SAR sensors calibration and multi-source data fusion.