TomoSAR 3D reconstruction: Cascading adversarial strategy with sparse observation trajectory

Abstract

Synthetic aperture radar tomography (TomoSAR) has shown significant potential for the 3D Reconstruction of buildings, especially in critical areas such as topographic mapping, urban planning, and disaster monitoring. In practical applications, the constraints of observation trajectories frequently lead to the acquisition of a limited dataset of sparse SAR images, presenting challenges for TomoSAR 3D Reconstruction and affecting its signal-to-noise ratio and elevation resolution performance. The study introduces a cascade adversarial strategy based on the Conditional Generative Adversarial Network (CGAN), optimised explicitly for sparse observation trajectories. In the preliminary phase of the CGAN, the U-Net architecture was employed to capture more global information and enhance image detail recovery capability, which is subsequently utilised in the cascade refinement network. The ResNet34 residual network in the advanced network stage was adopted to bolster feature extraction and image generation capabilities further. Based on experimental validation performed on the curated TomoSAR 3D super-resolution dataset tailored for buildings, the findings reveal that the methodology yields a notable enhancement in image quality and accuracy compared to other techniques.