Why Synthetic Infrared Data Is Hard — and Why It Matters for AI Systems
- Mohsen Zardadi
- Feb 4
- 6 min read
Synthetic Infrared Data: Challenges for AI, ISR, and ATR
Written by: Mohsen Zardadi and Luis Bolanos
Infrared (IR) imagery is a foundational sensing modality for many AI systems in surveillance, reconnaissance, and automatic target recognition (ATR). Unlike electro-optical (EO) imagery, IR captures thermal radiation, enabling detection and classification under conditions where visible-light sensors struggle.
At TerraSense Analytics, we build real-world, multi-modal AI systems such as MIST (Multi-Modal Input Surveillance & Tracking). Across projects, one constraint appears again and again: the availability of sufficient, representative infrared data—especially airborne IR data.
Three Ways AI Teams Get Training Data — and Their Limits
In practice, there are three dominant approaches for obtaining training data for AI systems:
1. Use existing datasets
Most AI teams start here. These datasets are often collected for other purposes and later repurposed for machine learning. Inevitably, they carry biases introduced by how, when, and why the data was collected. They also rarely match the exact operational conditions an AI system will encounter.
This is why modern AI workflows increasingly rely on large, generic pre-trained models that are later fine-tuned on limited application-specific data: teams know their base datasets are not perfectly aligned with their problem domain.
2. Run dedicated data-collection campaigns
This approach can produce high-quality, domain-specific data—but at a significant cost. Capturing sufficient variability in environment, time of day, weather, background, and target state requires continuous, large-scale collection.
The autonomous vehicle industry provides a clear example. Even with fleets of sensor-equipped vehicles operating continuously, covering the full space of real-world variability has required tens of billions of dollars and many years of effort. And even then, edge cases remain elusive.
For infrared sensing, where sensors are expensive and airborne platforms are constrained, this approach quickly becomes impractical.
3. Create the data yourself
The third option is to generate data synthetically. This offers a compelling promise: the ability to produce the data you need, when you need it, with controlled variability and at scale.
However, this option raises a critical question: what kind of synthetic data actually helps?
Synthetic Data Is Not a Single Thing
In practice, synthetic infrared data can be generated using fundamentally different approaches. In practice, we distinguish between two broad categories that are often conflated in discussion but have very different trade-offs: physics-based rendering and data-driven generative methods.
Understanding the strengths and limitations of each is critical when choosing how to scale IR training data for operational AI systems.
Physics-Based Rendering and Simulation
Physics-based rendering approaches generate synthetic IR data by explicitly modelling the physical processes that govern infrared phenomenology. These methods attempt to simulate how energy is emitted, absorbed, reflected, and transmitted through the environment, and how sensors respond to that energy.
Examples of this class of methods include:
Thermodynamic and radiative transfer models
Scene-level simulation environments that model materials, atmosphere, and sensor characteristics
Sensor-accurate rendering pipelines used for algorithm evaluation and system design
Strengths
Grounded in physical principles, enabling interpretable and controllable scene generation
Can produce rich ground truth, including metadata that is difficult or impossible to obtain from measured data
Particularly well suited for sensor analysis, phenomenology studies, and controlled experiments
Limitations
High development cost and long iteration cycles, especially for infrared where:
Thermal emission depends on time-varying heat transfer
Material properties vary significantly across wavelength bands
Environmental effects (solar loading, atmosphere, background coupling) dominate observed signatures
Scaling to broad operational diversity requires significant manual effort in scene construction, calibration, and validation
Small fidelity gaps in physics or materials can translate into systematic errors in downstream models
While physics-based rendering remains a powerful tool, its cost and complexity make it difficult to use as the primary mechanism for generating large-scale IR training datasets.
Data-Driven Generative Methods
Data-driven generative approaches take a fundamentally different path. Rather than explicitly modelling physical processes, these methods learn a statistical mapping from data—often translating from one modality to another.
In the context of EO-to-IR synthesis, this includes:
GAN-based image-to-image translation methods
More recent diffusion-based generative models
Conditional generation frameworks that preserve scene structure while altering modality
Strengths
Significantly lower barrier to scale compared to physics-based simulation
Able to leverage abundant EO imagery to address IR data scarcity
Well-suited for rapid iteration and dataset augmentation
When conditioned appropriately, can preserve geometric and semantic consistency between EO and IR representations
Limitations
Generative models are inherently bounded by the domain of their training data
They do not explicitly encode physical laws, which can lead to:
Inaccurate thermal contrast under certain conditions
Loss of fine-scale structure relevant to detection tasks
Visual realism alone is not sufficient to guarantee usefulness for downstream learning
In practice, there is no universally “correct” approach to synthetic data generation. The choice between physics-based rendering and data-driven generative models depends heavily on the application, available tooling, and operational constraints. When high-fidelity simulation environments are available—complete with accurate material models, environmental dynamics, and sensor characterization—physics-based rendering can provide controlled, interpretable data with rich ground truth. However, building and maintaining such simulations is costly, time-intensive, and often impractical outside of well-resourced programs.
In many cases, teams turn to data-driven generative models as a more scalable alternative. These methods offer faster iteration and the ability to leverage abundant data sources, particularly in the visible spectrum. The challenge, however, is that most generative models are trained primarily on electro-optical imagery. Extending them to infrared is not a straightforward modality swap. The physical processes that govern infrared signatures differ fundamentally from those in the visible domain, and models trained on vision data do not inherently capture these effects.
This distinction becomes especially important when moving from visible-spectrum sensing to infrared, where the underlying physics, sources of variability, and sensor responses are significantly more complex.
Why Infrared Is Fundamentally Harder Than Visible Spectrum
Most modern rendering and simulation pipelines were developed for the visible spectrum. Infrared sensing operates under very different physical constraints.
In IR:
Everything emits radiation based on temperature
Multiple sources contribute simultaneously, including solar loading, atmospheric radiance, object emission, conduction, and convection
Spectral bandwidths are wide, spanning short-, mid-, and long-wave infrared
Heat transfer matters, not just illumination
As a result, the same object in the same scene can exhibit drastically different signatures depending on time of day, environmental conditions, and operational state. A vehicle that appears “hot” relative to the background in one scenario may appear cold or indistinguishable in another.
This variability creates an enormous combinatorial space that is infeasible to cover exhaustively through measured data alone.
Why “Looking Real” Is Not the Same as Being Useful
A common pitfall in synthetic data workflows is evaluating success based on visual realism or image-similarity metrics. In infrared applications, this is particularly misleading.
Synthetic IR imagery can appear convincing while still failing to preserve:
Correct relative thermal contrast
Energy consistency across sensor pixels
Material- and band-specific behaviour
Sensor noise and responsivity characteristics
For AI systems, these deficiencies often only become apparent during downstream evaluation—when models trained on synthetic data underperform on real measurements.
For this reason, the value of synthetic IR data must be judged by its impact on model performance, not by appearance alone.
Why This Matters for Operational AI Systems
In systems like MIST, synthetic data is not a visualization aid—it is part of a broader strategy to build robust, deployable AI capabilities under real-world constraints.
To be useful, synthetic IR data must:
Reflect the underlying physics of infrared phenomenology
Preserve semantic and contextual consistency
Improve generalization and robustness in downstream models
Meeting all three simultaneously is difficult, but necessary. Synthetic data that fails on any one of these dimensions risks introducing false confidence rather than improving system behaviour.
Looking Ahead
In the next post, we will describe how we approached EO-to-IR data generation in practice, using a diffusion-based framework designed to balance scalability with control—without relying on dense, manual annotations.
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