Technical Report: Towards Spatial Feature Regularization in Deep-Learning-Based Array-SAR Reconstruction

TL;DR

This paper introduces a novel approach integrating spatial feature regularization into deep learning-based Array-SAR reconstruction, significantly improving 3D urban mapping accuracy and structural integrity.

Contribution

It develops a framework for modeling and regularizing spatial features in DL-based Array-SAR, enhancing reconstruction quality in urban environments.

Findings

Improved reconstruction accuracy with spatial feature regularization.

Enhanced structural integrity and completeness of building models.

Increased robustness against noise and outliers.

Abstract

Array synthetic aperture radar (Array-SAR), also known as tomographic SAR (TomoSAR), has demonstrated significant potential for high-quality 3D mapping, particularly in urban areas.While deep learning (DL) methods have recently shown strengths in reconstruction, most studies rely on pixel-by-pixel reconstruction, neglecting spatial features like building structures, leading to artifacts such as holes and fragmented edges. Spatial feature regularization, effective in traditional methods, remains underexplored in DL-based approaches. Our study integrates spatial feature regularization into DL-based Array-SAR reconstruction, addressing key questions: What spatial features are relevant in urban-area mapping? How can these features be effectively described, modeled, regularized, and incorporated into DL networks? The study comprises five phases: spatial feature description and modeling, regularization, feature-enhanced network design, evaluation, and discussions. Sharp edges and geometric shapes in urban scenes are analyzed as key features. An intra-slice and inter-slice strategy is proposed, using 2D slices as reconstruction units and fusing them into 3D scenes through parallel and serial fusion. Two computational frameworks-iterative reconstruction with enhancement and light reconstruction with enhancement-are designed, incorporating spatial feature modules into DL networks, leading to four specialized reconstruction networks. Using our urban building simulation dataset and two public datasets, six tests evaluate close-point resolution, structural integrity, and robustness in urban scenarios. Results show that spatial feature regularization significantly improves reconstruction accuracy, retrieves more complete building structures, and enhances robustness by reducing noise and outliers.