Can we generate a credible SAR image from an optical image?

Introduction

Simulating Synthetic Aperture Radar (SAR) imagery is an intellectually fascinating but conceptually demanding task. For researchers working in this field, the challenges are not only technical, but also philosophical. The very act of simulation raises fundamental questions about the nature and purpose of measurement.

On one hand, SAR simulation is a highly complex and delicate discipline. If we can synthesize realistic radar images, one might ask: why continue acquiring real data at all? What becomes of the scientific value of measurement if scenarios can be virtually generated on demand?

On the other hand, if we assert that nothing can ever replace real observation, how can we justify pursuing simulation as a legitimate research field rather than a mere visualization exercise? The truth, as always, lies somewhere in between. Simulation and measurement are not opposites but complementary tools — one probing our models, the other probing reality. Together, they deepen our understanding of how information, physics, and perception intertwine.

This tension has become even more pronounced in the age of deep learning. With the rapid rise of generative models, it is tempting to assume that neural networks can reproduce any modality, any physical process, any scene — that “image-to-image translation” frameworks could simply learn to convert optical into radar. Yet, behind this promise lie fundamental scientific challenges: physical consistency, semantic plausibility, and the intrinsic limits of inference when information is not directly observable. These are not merely technical obstacles but conceptual boundaries defining what it truly means to understand a signal.

In this context, the central question is not whether images can be generated — modern AI clearly shows they can — but rather what it means for such generated images to be physically and scientifically credible.
When dealing with radar, credibility is not only a matter of visual realism but of physical coherence: a simulated image must behave as a radar would, not merely look like one.
This brings us to a fundamental and practical question — the one guiding the rest of this discussion: can we generate a credible SAR image from an optical one?

What Makes a SAR Image “Credible”?

A Synthetic Aperture Radar (SAR) image can be considered credible only if it reproduces, in a physically and semantically consistent manner, the phenomena that an actual radar sensor would record.

Physical credibility involves consistency with the physics of radar imaging:

• Correct backscatter contrast between surfaces, reflecting moisture and dielectric properties.
• Speckle statistics consistent with coherence and number of looks.
• Geometry-dependent distortions — layover, foreshortening, and shadow — tied to the radar’s viewing geometry and the 3D structure of the scene.
• For advanced applications, phase coherence across acquisitions (e.g., interferometry), or polarization signatures consistent with known scattering mechanisms.

Semantic credibility complements the physical one. A synthetic image must also depict a plausible world. Even if radiometry and geometry appear realistic, an image showing impossible configurations — a high-rise building in the middle of the ocean, or a suspension bridge crossing a dense urban area — would instantly break the illusion of credibility.
When no strong conditioning input is available (for example, when generating directly from text rather than from optical guidance), maintaining this semantic realism becomes the key requirement for credibility.

In short, physical consistency ensures that the image “looks like radar,” while semantic consistency ensures that it “makes sense.” Both are indispensable for a simulation to be trusted.

Why You Can Plausibly Generate SAR from Optical Data

Appearance and radiometry can be approximated.
Given an optical image combined with auxiliary datasets — such as a digital elevation model (DEM), a land cover map, and surface moisture estimates — a model can predict which surfaces are likely to appear bright or dark in SAR images. For instance:

• Smooth water → low backscatter
• Vertical walls + ground → strong double-bounce
• Rough vegetation → medium backscatter

As spatial resolution increases, geometric effects become critical. Radar imaging is inherently dependent on acquisition geometry: look angle, azimuth, and slant-range all interact with the 3D structure of objects. Reproducing realistic layover, foreshortening, or shadowing patterns — especially around buildings or complex terrain — requires explicit consideration of both sensor geometry and scene topography.

In principle, this is achievable if the 3D structure of the scene and acquisition parameters are known, allowing a forward-projection of radar scattering consistent with real imaging conditions.

Speckle can be simulated statistically.
SAR speckle is a multiplicative, coherence-dependent phenomenon governed by well-defined statistics on homogeneous areas — Rayleigh or Nakagami distributions for amplitude, Gamma distributions for intensity with L corresponding to the number of looks. Generative models can inject speckle that follows these distributions, producing a visually consistent texture and conveying the characteristic “grain” of SAR images.

Machine learning can learn visual mappings.
Conditional generative models (GANs, diffusion models) trained on paired optical–SAR datasets can produce SAR-style outputs that are visually convincing, particularly at medium resolution and for common landscape types. Incorporating physical conditioning — such as DEM, incidence angle maps, polarization, or soil moisture proxies — significantly improves realism.

When combined with 3D geometry and acquisition metadata, these models can, at least in principle, reproduce both the radiometric and geometric patterns typical of (even high-resolution) SAR.

Why a Generated SAR Image Can Never Fully Replace Measurement

Phase information cannot be recovered from optical data.
Optical sensors measure reflected light intensity, not electromagnetic phase at microwave frequencies. The SAR phase — which encodes sub-wavelength path differences and enables interferometric applications — cannot be inferred deterministically. Simulated or randomized phase fields can only mimic, not reproduce, reality. Consequently, tasks that rely on phase coherence (e.g., deformation mapping, coherent change detection) cannot be achieved with generated data.

Measurements capture unknowns; generation cannot.
A real radar observation reveals hidden physical states — subsurface moisture, recent rainfall, buried structures — that may leave no optical signature. A model trained on optical data alone cannot infer such invisible variables. This distinction is crucial for domains like archaeology, hydrology, and post-disaster assessment.

Ambiguity and non-uniqueness.
Multiple physical configurations (geometry, permittivity, roughness, incidence angle) can yield similar backscatter. From a single optical view, several plausible SAR realizations exist, but only one corresponds to the true measurement. Generative models can thus produce a realistic SAR, but never the real one.

Current Directions for Increasing Physical Credibility

Improving the physical credibility of simulated SAR imagery remains an active area of research. Recent efforts focus less on producing visually convincing results and more on reproducing the underlying measurement physics — ensuring that simulated signals behave as real radar data would under comparable conditions. Several directions appear particularly promising:

1. Integration of 3D surface information
Accurate geometric realism requires explicit modeling of terrain and object structure. The use of detailed digital elevation models (DEMs) or full 3D reconstructions enables the correct reproduction of layover, foreshortening, and shadowing effects tied to radar geometry.

2. Precise representation of acquisition parameters
Credible simulation depends on reproducing the full sensor configuration: incidence angle, polarization, frequency, bandwidth, and resolution all shape the measured signal.

3. Better characterization of material and environmental properties
Land cover, vegetation height, soil type, and surface moisture are critical to realistic backscatter modeling. Ongoing studies aim to link these biophysical variables with dielectric properties and scattering behavior, often combining field measurements, radiative transfer models, and data-driven estimation.

4. Advanced scattering models
From empirical parameterizations to full electromagnetic solvers (Kirchhoff approximation, physical optics, small-perturbation models, or ray tracing), physics-based scattering simulation remains the cornerstone of SAR realism. Current research seeks to hybridize these models — coupling approximate but efficient formulations with deep-learning surrogates that emulate their physical behavior at reduced computational cost.

5. Statistical, polarimetric, and dynamic realism
Reproducing the statistical nature of speckle and the coherence of polarimetric or interferometric channels remains a major challenge. Beyond static texture and polarization behavior, research increasingly recognizes the importance of temporal and dynamic effects — such as decorrelation, scene evolution, and motion-induced phase variations.
Simulating these phenomena requires modeling how coherence degrades over time, how scatterers move or change between acquisitions, and how such dynamics translate into measurable phase or amplitude variations.

Recommended Workflows

Before any simulation effort begins, it is crucial to clarify what kind of realism one is seeking.
Do we aim to reproduce the sensor and its parameters — geometry, noise, radiometry?
Do we seek realism of the scene — the 3D structure, material properties, or complex scattering mechanisms such as vegetation volumes?
Or are we targeting specific features — like aircraft or ships — to enrich datasets for algorithm development?
Each of these goals implies a different balance between physical fidelity, computational cost, and interpretability.
Defining this intent explicitly is the foundation of credible simulation.

When the underlying physics of a phenomenon is not well understood or not yet well measured — as often happens with low-frequency vegetation scattering, volume coherence, or mixed dielectric effects — the most productive path is to return to the physical model itself.
Rather than relying solely on data-driven learning, exploring the physical mechanisms, refining scattering models, and confronting them with limited measurements often yields deeper insight and longer-lasting value.
In this sense, simulation serves not just to generate data, but to test hypotheses about the world: it becomes a controlled laboratory for the signal.

At the same time, data-driven methods can play a complementary role when the mechanisms are known but too complex to model explicitly.
They can help refine textures, radiometry, or context from multimodal inputs — optical, DEM, or sensor geometry — provided that the physical constraints are not ignored.
Combining both reasoning paths — physics when we do not understand, and learning when we do — is often the most robust approach.

Finally, validation remains the cornerstone of credibility.
Synthetic SAR images must be systematically compared with real acquisitions whenever possible, not only to assess agreement but to reveal where and why discrepancies occur.
Validation is as much about recognizing uncertainty as about measuring accuracy.
Knowing what remains uncontrolled — in radiometry, geometry, or coherence — is an integral part of mastering simulation.
As in artificial intelligence more broadly, progress depends not on eliminating uncertainty, but on cultivating the discernment to recognize where it matters and where it does not.

Takeaway and Recommendation

Yes — SAR-looking images generated from optical data can be visually credible and useful for training data augmentation, visualization, or qualitative analysis.
No — such images cannot replace true radar measurements when those measurements probe unknown or non-visible physical states.
The best use is to treat synthetic SAR as a complementary tool — for prototyping algorithms, enriching datasets, and exploring physical priors — but never as a substitute for real observations when scientific inference or operational decisions are at stake.

Philosophical Perspective: Simulation as a Path to Understanding

Beyond its technical utility, simulating radar images has a deeper scientific and philosophical value: it pushes us to refine our understanding of what the radar signal truly encodes.

When our simulations fail to reproduce a measured signal, that failure is instructive.
For instance, if low-frequency radar returns over vegetation cannot be matched by a random-volume model, it reveals that our model is incomplete: at such wavelengths, vertical trunks must be explicitly represented to explain the observed scattering. Similarly, if a mission like BIOMASS detects unexpected precipitation effects, this suggests that our propagation or dielectric models still overlook essential processes.

Conversely, physics-based models like the RVoG (Random Volume over Ground) serve as conceptual tools that guide inversion algorithms — teaching us what aspects of the signal are physically meaningful and retrievable.

Another important dimension is data compression and information content.
If we can simulate 99% of a polarimetric channel’s behavior using another channel, it implies that only 1% of that channel’s information is truly independent. This insight leads us toward optimal measurement strategies: seeking the irreducible core of physical information.
Likewise, if optical and radar data overlap significantly in certain conditions, we can reduce redundancy — focusing on what each modality uniquely contributes.

In that sense, the latent space learned by deep networks mirrors this quest for optimal compression: discovering what makes a measurement unique and irreplaceable.
To simulate well is to understand what matters most — to capture the essence rather than the abundance of data.
And in an era where scientific and environmental frugality are becoming imperatives, simulation might well be part of the path toward “less but better” data — extracting not more information, but the most meaningful one.

This post offers a personal reflection on themes that emerge from ongoing research in the field.
These topics are explored in a more formal and scientific manner in the forthcoming book chapter:

Élise Colin, Nicolas Trouvé,
Deep learning for artificial SAR image generation: Generative AI for SAR,
in Deep Learning for Synthetic Aperture Radar Remote Sensing,
edited by Michael Schmitt and Ronny Hänsch, Elsevier, 2026, pp. 75–97.
https://doi.org/10.1016/B978-0-44-336344-3.00009-X