# Radar Interferometry using Two Images with Different Resolutions

## TL;DR

This paper introduces a novel dual-resolution radar interferometry method that reconstructs high-resolution interferograms from mixed-resolution images using compressive sensing and wavelet sparsity, reducing data acquisition requirements.

## Contribution

It formulates dual-resolution interferometry as a compressive sensing problem leveraging wavelet sparsity, enabling high-resolution results from mixed-resolution data.

## Key findings

- Feasible to recover high-resolution interferograms from mixed-resolution images.
- Reduces resolution and data acquisition requirements compared to traditional methods.
- Validated with Sentinel-1 data experiments.

## Abstract

Radar interferometry usually exploits two complex-valued radar images with the same resolution to extract terrain elevation information. This paper considers the interferometry using two radar images with different resolutions, which we refer to as dual-resolution radar interferometry. We find that it is feasible to recover a high-resolution interferogram from a high-resolution image and a low-resolution one. We formulate the dual-resolution interferometry into a compressive sensing problem, and exploit the wavelet-domain sparsity of the interferogram to solve it. Due to the speckle effect in coherent radar imaging, the sensing matrix of our model is expected to have small mutual coherence, which guarantees the performance of our method. In comparison with the conventional radar interferometry methods, the proposed method reduces the resolution requirement of radar image acquisition. It therefore can promote wide coverage, low sampling/data rate and storage cost. Numerical experiments on Sentinel-1 data are made to validate our method.