Modified oriented dihedral model for scattering characteristic description with PolSAR data

Abstract

Dihedral is a common structure in polarimetric SAR images and can be found on many man-made targets. Many researchers have proposed different dihedral models, but the accuracy of these models is limited. In this case, the feature extraction methods based on these models are also not effective enough, which affects the subsequent applications such as target detection. Therefore, it is necessary to propose a new and accurate scattering model, which can be applied to dihedral with different orientation angles for feature extraction and target detection. In this paper, a general scattering model called modified oriented dihedral scattering model (MODM) is proposed based on physical optics (PO) and geometric optics (GO) of high-frequency approximation techniques. By analyzing the propagation and reflection of electromagnetic wave, MODM can accurately describe the scattering characteristic of dihedral for all observation conditions. In order to apply the model to real PolSAR images, MODM is introduced into a new feature extraction method, which is called five-scattering component polarimetric decomposition method (MODM-5SD). Feature extraction and target detection experiments of buildings with various oriented dihedral structures are performed using different data sets, which show that dihedral scattering components from oriented dihedral structures can be more effectively extracted by MODM-5SD. In addition, more buildings with oriented dihedral structures can be detected with the features from MODM-5SD. The experimental results show that MODM can more accurately describe the scattering characteristic of dihedral, which can be further applied for scattering characterization and feature extraction of targets with typical dihedral structures.

This work was supported in part by the National Natural Science Foundation of China under Grant 62271172 and Grant 61871158, and in part by Aeronautical Science Foundation of China under Grant 20182077008.

Peer Reviewed

Postprint (published version)