A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model

Remote Sensing

Wenxue Fu¹  Xiujuan Li²  Yongxin Liu³  Xiaolong Liu⁴  Chunming Li⁴  Pingping Huang⁴  Weixian Tan⁴ 

[1] Aerospace Information Research Institute, Beijing 100094, China;College of Computer Science, Inner Mongolia University, Hohhot 010021, China;College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China;College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China;

关键词: hybrid polarimetric target decomposition;    generalized volume scattering model (GVSM);    random particle cloud model (RPCM);    adaptive volume scattering model;    polarimetric synthetic aperture radar (PolSAR);   

DOI  :  10.3390/rs14102441

来源: DOAJ

【 摘 要 】

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues.

【 授权许可】

Unknown