## 3 Experiments
By using the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method, the capability of the GF-3 data for distinguishing different scattering types is improved.In the Radarsat-2 image, the ocean surface is classified into two scattering mechanisms, which is because the ocean surface area outlined in Figure 3 is highly affected by the adjacent double-bounce scattering, while in the GF-3 image, this area is not affected by the adjacent strong double-bounce backscatter. This may be due to the smaller incidence angle and the suppressing method used for sidelobes or ambiguities in GF-3 imaging. Figure 2: _Cont._  
We randomly selected 500 samples from each typical themes and Figure 4 gives the \(H/alpha\) and the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) scatter plots. It is observed that by using the GF-3 data, samples from the ocean surface distributed more concentratedly than those of the Radarsat-2 data, and are more easily distinguished from the other theme pixels. This shows that the GF-3 data performs better in analyzing the physical scattering mechanism of ocean surface compared with the Radarsat-2 data. In both the \(H/alpha\) and \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) diagrams, results of the Rasarsat-2 data shows that more pixels distribute in the zones with higher \(alpha\) and \(\Delta\alpha_{\rm B}\) values in comparison with the results of the GF-3 data. By using the outlined four typical terrain themes, Table 1 gives the percentages of the classified scattering mechanisms of each area. \(zi\)\((i=1\cdots 9)\) denotes classification zone \(i\) (refer to Figure 1 for more details). The classification accuracy (CA) is calculated based on the physical interpretation of the predominant scattering mechanisms in each area. The CA for ocean is evaluated from the surface scattering, and thus pixels fall in zone 9 are used for the calculation of CA. Similarly, pixels in zone 5 and zone 2 are used to calculate the CA of forest, which is represented by the multiple vegetation scattering. Pixels in zone 7 and zone 4 are used to calculate the CA of the urban area, which is represented by the multiple even-bounce scattering. It is observed that the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method provide higher overall classification accuracies for both data sets compared with the \(H/alpha\) method in identifying the predominant scattering types. By using the same method, the overall classification accuracy of the GF-3 data is  
Figure 2: The Pauli-basis images of (**a**) GF-3 data collected on 15 September 2017, and (**b**) Radarsat-2 data collected on 9 April 2008. The outlined areas by white polygons are typical terrain types representing the ocean surface (1), forest (2), urban (3), and tilted urban (4) areas. (**c**) Google Earth image obtained on 25 September 2008, (**d**) Google Earth image obtained on 17 June 2017.  
not as good as that of the Radarsat-2 data. This implies that only the \(H/alpha\) plane or the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) diagram is not sufficient for GF-3 image classification. Figure 4: _Cont._  
Figure 3: Physical scattering mechanism classification results by (**a**) the \(H/alpha\) method with GF-3 data; (**b**) the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method with GF-3 data; (**c**) the \(H/alpha\) method with Radarsat-2 data; and (**d**) the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method with Radarsat-2 data. The ocean surface in the white circle is easily affected by targets with strong backscatter on land. Table 2 gives the statistical values of the physical parameters for the four typical areas. The mean value and the standard deviation are useful indicators for evaluating the between-class distance and with-in class deviation. For the purpose of classification, generally it is better to have a large between-class distance and a small with-in class deviation. We can observe that for the four themes, the discrimination abilities of parameter \(H\) of the GF-3 data and the Radarsat-2 data behave very similar to the standard deviation.