研究目的
To propose a novel algorithm for detecting CR-like targets and estimating the co-pol channel imbalance phase in uncalibrated PolSAR imagery as an alternative to ground-deployed corner reflectors.
研究成果
The proposed algorithm effectively estimates the co-pol channel imbalance phase using CR-like targets, with errors as low as 1.305° and 0.03° in experiments. It serves as a viable alternative when ground CRs are unavailable, improving PolSAR data quality, especially for sensors like Gaofen-3. Future work will focus on identifying real-world objects that generate CR-like targets and applying the method to more beam waves.
研究不足
The method depends on user-defined thresholds and parameters, which may require empirical tuning. Performance is better in high-resolution images with sufficient CR-like targets. It does not replace ground-deployed CRs in standard calibration chains and may be less effective in areas without manmade structures.
1:Experimental Design and Method Selection:
A step-by-step algorithm is designed to detect CR-like targets and estimate the co-pol channel imbalance phase. It includes preliminary calibration using the Quegan method, candidate generation via SCR filtering, CR-like target detection through crosstalk disturbance testing, and phase estimation using PFLFO.
2:Sample Selection and Data Sources:
Chinese X-band airborne PolSAR data from IECAS and C-band satellite PolSAR data from Gaofen-3 are used. Data include images with ground-deployed CRs for validation.
3:List of Experimental Equipment and Materials:
PolSAR systems (X-band IECAS airborne sensor, C-band Gaofen-3 satellite), corner reflectors (trihedral and dihedral), and computational tools for data processing.
4:Experimental Procedures and Operational Workflow:
Steps involve multi-look processing, classification of pixels, SCR filtering, MCR testing, diversity measure calculation, and PFLFO for phase fitting. Parameters like patch size and thresholds are user-defined based on image characteristics.
5:Data Analysis Methods:
Statistical techniques include least-squares linear regression for phase fitting, coherence estimation, and error calculation using mean absolute phase difference.
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