Polarimetric inverse scattering using LSTM-aided association-learning sparse reconstruction framework

Abstract

Conventional polarimetric inverse scattering is under the assumption of joint sparsity of different polarization channels. However, in practical situation, there are strong correlations among these multi-channel measurements, but the inherent dependencies are unknown; the deterministic joint sparsity assumption may cause insufficient utilization of association information among multi-polarization channels, and consequently lead to limited improvement of canonical scatterers extraction accuracy. Aimed at this problem, we propose a long short-term memory (LSTM)-aided association-learning sparse reconstruction framework for polarimetric inverse scattering, in which the dependencies among multi-channel measurements are captured via a data driven pattern. In the proposed scheme, instead of directly solving the full inverse problem, polarimetric inverse scattering with multi-attribute canonical scatterers extraction is implemented via hierarchical pattern, and consequently the required computational burden is reduced. Particularly, the LSTM network is utilized to perform atomic screening for estimating first type of attributes of canonical scatterers, and then the optimal adaptive filtering (OAF) process is carried out for estimating second and third types of attributes, followed by residual update process. Extensive experimental results demonstrate that the proposed approach is able to outperform existing algorithms in terms of reconstruction accuracy and computational speed.