[Paper] Beyond Backscatter: InSAR coherence from detected SAR images
Source: arXiv - 2606.07374v1
Overview
In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is trained using coherence maps derived from precisely coregistered Sentinel-1 SLC data to learn the relationship between backscatter magnitudes and coherence. The model is trained on 12-day SLC pairs and evaluated across different datasets, including coregistered SLC products and open access analysis-ready data, covering diverse radiometric properties, geometries, and locations. Experimental results demonstrate that the proposed method achieves high-resolution coherence regression with improved accuracy compared to existing intensity-based approaches. The network generalizes well across diverse geographical locations and even across different temporal baselines that were never seen at training time. Additionally, the ability to operate on globally available analysis-ready data, such as ground range detected data, e.g., distributed through Google Earth Engine, enables its large-scale application in mission design, change monitoring, and diverse mapping tasks.
Key Contributions
This paper presents research in the following areas:
- eess.SP
- cs.CV
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of eess.SP.
Authors
- Francescopaolo Sica
- Andrea Pulella
- Michael Schmitt
Paper Information
- arXiv ID: 2606.07374v1
- Categories: eess.SP, cs.CV
- Published: June 5, 2026
- PDF: Download PDF