Exploiting Inverse SAR Images and Dual-Pol Decomposition for the Estimation of Tree Scattering Properties

The Inverse Synthetic Aperture Radar (ISAR) provides images of objects that are rotating with respect to the radar. An efficient image focusing algorithm is required to generate ISAR imagery from the echoes of raw data. On the other hand, the dual-polarization decomposition technique enables precise retrieval of scattering mechanisms (H-α), allowing for various applications. In this paper, we propose a novel study case of 2D ISAR imaging of partial polarimetric data of natural targets. First, a stack of 2D complex-valued raw data with VV and VH polarizations is calibrated, and then the image focusing is applied using a match-filter and spherical-wave front compensation (SWFC) method. The eigenvector descriptors based decomposition is employed, and the scattering mechanism is identified using the Lee and Pottier H-α plane. To the best of the authors' knowledge, ISAR images are used for the first time for this study. Given that decomposition enhances target characterization for studying scattering mechanisms, the application of the Radar Vegetation Index (RVI) demonstrates how dual-polarized ISAR images can be used for vegetation identification.