Interpretable Attributed Scattering Center Extracted via Deep Unfolding

Most existing sparse representation based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation time and limited precision. This paper presents a solution by introducing an interpretable network that ca...

Full description

Saved in:
Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 2004 - 2008
Main Authors Yang, Haodong, Huang, Zhongling, Zhang, Zhe
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Attributed Scattering Center
Deep Unfolding
Dictionaries
Geoscience and remote sensing
Iterative algorithms
Neural networks
Reliability
Scattering
Sparse approximation
Sparse Representation
Online AccessGet full text
ISSN2153-7003
DOI10.1109/IGARSS53475.2024.10641709

Cover

More Information
Summary:Most existing sparse representation based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation time 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 iterative shrinkage-thresholding algorithm (ISTA). Then, we unfold ISTA to a neural network, employing it to autonomously and precisely optimize the hyperparameters. The interpretability in physics is retained by applying a dictionary with physical meaning. The experiments are conducted on multiple test sets with diverse data distribution and demonstrate the superior performance and generalizability of our method.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10641709