Polarimetric Correlation Coefficient Applied to Tree Classification(Special Issue on New Technologies in Signal Processing for Electromagnetic-wave Sensing and Imaging)

Tree canopies contain various scattering elements such as leaves, branches and trunks, which contribute to complex back-scattering, depending on frequency and polarization. In this paper, we propose to use the polarimetric correlation coefficient for classifying trees, forests, and vegetations. The polarimetric correlation coefficient can be derived by the elements of Sinclair scattering matrix. Since the scattering matrix can be defined in any polarization basis, we examined the coefficient in the linear HV, circular LR, and optimum polarization bases. First, the change of correlation coefficient inside trees along the range direction is examined using small trees in a laboratory. The wider the range, the better the index. The coefficient defined in the LR polarization basis showed the largest change within tree canopy, which also contribute to retrieve scattering mechanism. Second, this index for discrimination is applied to polarimetric SAR data sets(San Francisco and Briatia area) acquired by AIRSAR and SIR-C/X-SAR. It is shown that polarimetric correlation coefficient in the LR basis best serves to distinguish tree types