Interpretable attributed scattering center extracted via deep unfolding

TL;DR

This paper introduces an interpretable deep unfolding network for rapid and precise extraction of attributed scattering centers, leveraging physical dictionaries to enhance interpretability and performance.

Contribution

It proposes a novel deep unfolding approach that combines physical dictionaries with neural networks for efficient ASC extraction, improving over traditional iterative methods.

Findings

Superior accuracy over traditional methods

Fast computation times

Good generalization across diverse datasets

Abstract

Most existing sparse representation-based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation times and limited precision. This paper presents a solution by introducing an interpretable network that can effectively and rapidly extract ASC via deep unfolding. Initially, we create a dictionary containing reliable prior knowledge and apply it to the iterative shrinkage-thresholding algorithm (ISTA). Then, we unfold ISTA into a neural network, employing it to autonomously and precisely optimize the hyperparameters. The interpretability of physics is retained by applying a dictionary with physical meaning. The experiments are conducted on multiple test sets with diverse data distributions and demonstrate the superior performance and generalizability of our method.