Real-World DEM Super-Resolution Based on Generative Adversarial Networks for Improving InSAR Topographic Phase Simulation

Abstract

Topographic phase simulation is important for deformation estimation in differential synthetic aperture radar (SAR) interferometry (DInSAR). The most commonly used 30-m resolution SRTM digital elevation model (DEM) is usually required to be resampled due to its relatively low resolution (LR) comparing to the high resolution (HR) SAR images. Although the WorldDEM TM with a 12-m resolution achieves global coverage, it is not available freely. Consequently, it is useful to evaluate the practicability of the super-resolution (SR) from LR SRTM DEMs to HR WorldDEM TM ones, which has not been investigated. Most existing DEM SR models are trained with synthetic datasets in which the LR DEMs are downsampled from their HR counterparts. However, these models become less effective when applied to real-world scenarios due to the domain gap between the synthetic and real LR DEMs. In this paper, we constructed a realworld DEM SR dataset where the LR and HR DEMs were collected from SRTM and WorldDEM TM , respectively. An ESRGAN model was adapted to train on the dataset. Considering that the real LR-HR pairs may suffer from misalignment, we introduced the perceptual loss for better optimizing the model. Moreover, a logarithmic normalization was proposed to compress the wide elevation range and adjust the uneven distribution.We also pretrained the model using natural images since collecting sufficient HR DEMs is costly. Experiments demonstrate that the proposed method achieves near 0.69dB improvement of peak signal-to-noise ratio (PSNR). In addition, our method is also validated to improve the topographic phase simulation by 23.42% of MSE.

A Coherence-Guided InSAR Phase Unwrapping Method With Cycle-Consistent Adversarial Networks

“…Because of the characteristics of radar imaging, the influence of all kinds of noises brought by the spatio-temporal decorrelation on the data, such as the interferogram and the unwrapped phase, can only be eliminated as much as possible but cannot be avoided completely. Wu et al [45] studied the superresolution of DEM based on GAN, which generated the WorldDEM with a resolution of 12 m from the shuttle radar topography mission (SRTM) DEM with a resolution of 30 m. The composition of the real DEM dataset was so limited that the strategy of transfer learning was adopted. Zhou [19] introduced the PU method with cGAN (PU-GAN) to generate the unwrapped phase from the interferogram.…”

An Enhanced Residual Feature Fusion Network Integrated with a Terrain Weight Module for Digital Elevation Model Super-Resolution

“…Therefore, the network trained with paired data generated using interpolation-based methods only simply fits the inverse process of interpolation, which lacks practical significance in the real world. However, the significance of taking real-world low-resolution DEM datasets as training datasets instead of data obtained using downsampling from high-resolution data has been overlooked thus far [38].…”

A Novel Adversarial Learning Framework for Passive Bistatic Radar Signal Enhancement

“…GAN is an effective generative model based on game theory, and various GAN versions have been proposed for different tasks, such as image-to-image translation [28], speech enhancement [29], classification [30][31][32], sample generation [33,34], redundant information mitigation [35][36][37], and image dehazing [38]. Moreover, GAN has been applied to various radar systems, such as synthetic aperture radar (SAR) [39][40][41][42], inverse synthetic aperture radar [43,44], LPI radar [45], and weather radar [46].…”