## 1 Introduction  
Synthetic aperture radar (SAR) has all-weather, day and night imaging capabilities. In the past twenty years, a number of space-borne SAR systems designed for various Earth observation missions have been launched into Earth orbit. Typical polarimetric SAR systems include C-band RADARSAT-2, C-band Sentinel-1, L-band ALOS-2/PALSAR, X-band TerraSAR-X/TanDem-X, and the X-band COSMO-SkyMed satellite constellation. In August 2016, China launched the GF-3 satellite, its first polarimetric SAR satellite, which was in operation since January 2017. The GF-3 satellite has on-board a C-band high resolution SAR. It can work in twelve different imaging modes, in which the highest resolution is up to 1 m. It can provide fully polarimetric measurements with incidence angle ranging from 20\({}^{\circ}\) to 41\({}^{\circ}\), and the antenna look direction can be either right or left. Nowadays, more and more polarimetric SAR satellites are going to be launched and planed. Efficient usage of these polarimetric SAR images is becoming a crucial problem.  
Only a few studies on the use of GF-3 polarimetric SAR data for Earth observation applications can be found in the open literature. [PERSON] et al. [1] used the rational polynomial coefficient model to study the geometric accuracy of GF-3 imagery. [PERSON] et al. [2] investigated the application of GF-3 data for extracting information of ocean internal waves. [PERSON] et al. [3] showed that GF-3 data is effective for fast vessel detection. [PERSON] et al. [4] combined the parameters from Cloude-Pottier's decomposition with the convolutional network for GF-3 image classification. The application performance of GF-3 polarimetric SAR data still needs to be fully assesses.  
Polarimetric SAR can fully characterize the backscattering property of targets by using two orthogonal polarization channels, which allows development of different scattering models for different kinds of scatterers. Polarimetric feature extraction is fundamental for polarimetric target interpretation. Many parameter retrieval techniques and target decomposition methods [5; 6; 7; 8; 9; 10; 11; 12] in terms of both coherent and incoherent categories have been developed for target feature extraction. Among the decomposition methods, the Cloude-Pottier decomposition provides a simple but effective method to identify the target scattering mechanisms. This method has been investigated and used widely in many applications. [PERSON]'s four component decomposition [10] divides the backscattered energy into four different scattering mechanisms based on four scattering models and is widely used for polarimetric SAR image interpretation. In [13], based on the co-polarization ratio, we proposed a new physical scattering mechanism classification method to explore the intrinsic relationship between a single scattering mechanism and the stochastic backscattering process. This method was proposed based on the assumption of reflection symmetry, which is usually valid for natural scatterers, while for urban areas, backscatter can be both reflection symmetric and asymmetric due to the complexity of urban structures and alignments. In this paper, we further analyze the method presented in [13] under the reflection asymmetry condition. Then, together with Cloude-Pottier's decomposition [6; 14], this method is applied to analyze the performance of GF-3 polarimetric SAR data for representing target physical scattering mechanisms and terrain type classification capability.  
The study is organized as follows: in Section 2, the method is introduced and analyzed for both reflection symmetry and asymmetry cases. In Section 3, by using both GF-3 polarimetric SAR data and Radarsat-2 polarimetric data collected over San Francisco area in the USA, several experiments are carried out. Conclusions are drawn in Section 4.  
The Co-Polarization Ratio-Based Parameters and the \(\boldsymbol{\Delta\alpha_{B}/\alpha_{B}}\) Diagram