This PDF file contains the front matter associated with SPIE Proceedings Volume 12270, including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

With the increased threat from advanced sensor systems using several wavelength bands and sensor fusion, the requirements on camouflage increase. Consequently, in order to reduce the risk of EU soldiers being detected, identified and engaged, adaptive camouflage prototypes have been developed, implemented and demonstrated.

Several active and passive adaptation mechanisms were integrated into a textile-based soldier camouflage system. The prototypes address several wavelength bands, including visual, thermal infrared and radar. Military needs on sensing, fire power, mobility, compatibility with other equipment and wearing endurance were considered.

The demonstration, at TRL5, took place in a relevant environment, an area adjacent to vegetation and buildings close to Paris, France. The reduction in detection range and hence the increase in survivability, as established in field trials previously conducted in Portugal and France, was made clear during the demonstration. The demonstrated technology comprised actively controlled light emitting diodes and thermochromic materials for visible signature adaptation, phase change materials and biologically degradable insulating cellulose cells (cork) for thermal camouflage and radar absorbing coatings with conjugated polymer compositions. Two suits were shown with soldiers walking along a treeline and in front of buildings.

Prior to the public demonstration laboratory tests of the materials and components had been performed as well as ergonomic tests, signature measurements in different backgrounds and light conditions and radar cross section measurements at an outdoor test range.

Production technologies were studied and production cost estimated. It was concluded that a similar system can be produced at a reasonable cost and the production scaled up to larger volumes.

Throughout the project, dialogue with military end-users was upheld concerning requirements and applicable environments and tactical situations in order to ensure relevance. Military end-users from Sweden, Portugal, Germany, France, Lithuania and the Netherlands were invited to the final workshop and technical demonstration.

Natural scenes are typically highly heterogeneous, making it difficult to assess camouflage effectiveness for moving objects since their local contrast varies with their momentary position. Camouflage performance is usually assessed through visual search and detection experiments involving human observers. However, such studies are time-consuming and expensive since they involve many observers and repetitions. Here, we show that a (state-of-the-art) convolutional neural network (YOLO) can be applied to measure the camouflage effectiveness of stationary and moving persons in a natural scene. The network is trained on human observer data. For each detection, it also provides the probability that the detected object is correctly classified as a person, which is directly related to camouflage effectiveness: more effective camouflage yields lower classification probabilities. By plotting the classification probability as a function of a person’s position in the scene, a ‘camouflage efficiency heatmap’ is obtained, that reflects the variation of camouflage effectiveness over the scene. Such a heatmap can for instance be used to determine locations in a scene where the person is most effectively camouflaged. Also, YOLO can be applied dynamically during a scene traversal, providing real-time feedback on a person’s detectability. We compared the YOLO-predicted classification probability for a soldier in camouflage clothing moving through a rural scene to human performance. Camouflage effectiveness predicted by YOLO agrees closely with human observer assessment. Thus, YOLO appears an efficient tool for the assessment of camouflage of static as well as dynamic objects.

Hyperspectral sensors are used to measure the electromagnetic spectrum in hundreds of narrow and contiguous spectral bands. The recorded data exhibits characteristic features of materials and objects. For tasks within the security and defense domain, this valuable information can be gathered remotely using drones, airplanes or satellites. In 2021, we conducted an experiment in Ettlingen, Germany, using a drone-borne hyperspectral sensor to record data of various camouflage setups. The goal was the inference of camouflage detection limits from typical hyperspectral data evaluation approaches for different scenarios. The experimental site is a natural strip of vegetation between two corn fields. Our main experiment was a camouflage garage that covered different target materials and objects. The distance between the targets and the roof of the camouflage garage was modified during the experiment. Together with the target variations, this was done to determine the material dependent detection limits and the transparency of the camouflage garage. Another experiment was carried out using two different types of camouflage nets in various states of occlusion by freshly cut vegetation. This manuscript contains a detailed experiment description, as well as, the first results of the camouflage transparency and occlusion experiment. We show that it is possible to determine the target inside the camouflage garage and that vegetation cover is not suitable additional camouflage for hyperspectral sensors.

In the last years AI based algorithms have significantly increased in both popularity and in efficiency for numerous applications. As those artificial neuronal networks can also be used for military reconnaissance, is it necessary to think about methods to avoid or impede enemy detection or recognition by automated AI systems. However, the features that make an object salient to a human observer are not transferable to AI-based systems, since the features that the AI uses to classify things are mostly learning-data dependent and obscure. In this work, we aim to show ways to understand AI's decisions using LIME or Grad-CAM, and thus find ways to decrease classification performance in order to develop a camouflage against AI, or to decept it with adversarial attacks. Camouflage measures can then be evaluated using these methods for their effectiveness against AI, and by combining this with camouflage performance evaluation against human observers using existing methods we try to find the best possible tradeoff for combined camouflage against both threats.

The ability to model and simulate target signatures in a range of backgrounds under a range of environmental conditions are highly desirable as it allows for better understanding and assessment of the vulnerability of our soldiers in the battlefield. One main limitation of using game engine simulation is that they have limited capabilities and fidelity in simulating thermal signatures of targets and background. Physics based simulation software program on the other hand is more capable of simulating thermal properties of the scene and rendering a more physically realistic sensor image. In this study, we used MuSES (ThermoAnalytics), a physics based EO/IR signature simulation software to model and simulate the thermal properties and predicated thermal imagery of a real world scene with moving human targets. We used FLIR thermal IR cameras to capture the scene and the same scene was simulated in MuSES. A comparison of the synthetic and real thermal IR imagery was conducted and the progress made towards creating realistic thermal imagery in the land domain discussed

Evaluating the visible signature of operational platforms has long been a focus of military research. Human observations of targets in the field are perceived to be the most accurate way to assess a target’s visible signature, although the results are limited to conditions observed in the field. Synthetic imagery could potentially enhance visible signature analysis by providing a wider range of target images in differing environmental conditions than is feasible to collect in field trials.

In order for synthetic images to be effective, the virtual scenes need to replicate reality as much as possible. Simulating a maritime environment presents many difficult challenges in trying to replicate the lighting effects of the oceanic scenes precisely in a virtual setting. Using the colour checker charts widely used in photography we present a detailed methodology on how to create a virtual colour checker chart in synthetic scenes developed in the commercially available Autodesk Maya software. Our initial investigation shows a significant difference between the theoretical sRGB values calculated under the CIE D₆₅ illuminant and those simulated in Autodesk Maya under the same illuminant. These differences are somewhat expected, and must be accounted for in order for synthetic scenes to be useful in visible signature analysis. The sRGB values measured from a digital photograph taken at a field trial also differed, but this is expected due to possible variations in lighting conditions between the synthetic and real images, the camera’s sRGB output and the spatial resolution of the camera which is currently not modelled in the synthetic scenes.

To create a complete virtual scene with sufficient fidelity so it can be used for signature/camouflage assessment turns out to be complicated. Small differences in scene parameters can have a huge impact on the outcome and parameters are often unknown, which makes comparison with real environments/scenes difficult. Instead of fully virtual scenes we explore ‘hybrid’ or ‘composite’ methods in which a virtual target is simulated in the context of recorded imagery. It turns out that such techniques can generate highly realistic scenes. Here, we explored and compared various methods to create a virtual target: i) small physical models of the target, ii) Material Capture (matcap) in which a probe sphere is recorded in the scene and iii) light field measurements in combination with BRDF. Alongside the virtual target we collect images of the real target in field trials to be used for comparison and evaluation. We discuss the results and compare the different methods. Certain methods seem more applicable to derive a robust measure of the signature of a given target (e.g. the physical model and ‘matcap’), while others seem more appropriate for deriving and testing new camouflage designs and concepts (e.g. light field method).

Thermal image formation can be modeled as the convolution of an ideal image with a point spread function (PSF) that characterizes the optical degradations. Although simple space-invariant models are sufficient to model diffraction-limited optical systems, they cannot capture local variations that arise because of nonuniform blur. Such degradations are common when the depth of field is limited or when the scene involves motion. Although space-variant deconvolution methods exist, they often require knowledge of the local PSF. In this paper, we adapt a local PSF estimation method (based on a learning approach and initially designed for visible light microscopy) to thermal images. The architecture of our model uses a ResNet-34 convolutional neural network (CNN) that we trained on a large thermal image dataset (CVC-14) that we split in training, tuning, and evaluation subsets. We annotated the sets by synthetically blurring sharp patches in the images with PSFs whose parameters covered a range of values, thereby producing pairs of sharp and blurred images, which could be used for supervised training and ground truth evaluation. We observe that our method is efficient at recovering PSFs when their width is larger than the size of a pixel. The estimation accuracy depends on the careful selection of training images that contain a wide range of spatial frequencies. In conclusion, while local PSF parameter estimation via a trained CNN can be efficient and versatile, it requires selecting a large and varied training dataset. Local deconvolution methods for thermal images could benefit from our proposed PSF estimation method.

In this study, ground based image sequences of the sky will be evaluated to analyse the cloud coverage. These images are taken in the visual and infrared spectrum. The main ambition is to determine the cloud coverage without the knowledge of additional measurements (like temperature or precipitable water vapor). The determination of the cloud coverage is deduced from camera images only. In the visual spectrum, methods from literature are extended according to this application. For example, the ratio of the color channels red and blue is formed. In the infrared spectral range a method is developed that can distinguish the cloud-covered from the cloudless image areas by using the maximum and minimum occurring values in the image. The grey values are parameterised using statistical boundary values in such a way that a temperature relationship is unambiguously possible and consequently a statement about the degree of coverage can be made by the algorithm. The determination of the cloud coverage reaches a higher accuracy and reliability in the infrared spectral range.

Performance evaluation of C-UAS sensor systems and the development of detection algorithms require ground truth knowledge of the position of the target drone to be available during the measurement. In practice this is done by retrieving flight logs from the UAS, and comparing the obtained GNSS positions with the known position and orientation of the sensor at the corresponding timestamps in the recorded sensor data. Alternatively, video or sensor data can be manually annotated by an operator to indicate whether a drone is seen in each frame, allowing sensor performance to be evaluated.

These methods are time consuming and can be prone to introduce errors into the process. Also, not all UAV platforms allow for these data to be readily accessible. In our approach we use the open source MAVLink protocol used in Ardupilot-based UAVs to implement a trial range framework, which communicates between drones in flight, the ground control station, sensor platform trackers and optionally GNSS tracker modules to build a real time representation of the full trial setup, in order to provide unambiguous ground truth with known accuracy at all times. We demonstrate this setup, which will allow us to perform consistent evaluations of sensor systems in future trials.

The ability to camouflage an object in different wavelength regions is essential in order to avoid detection by sensors. As sensor technologies constantly develop, the need for efficient military camouflage techniques increases. Painting vehicles and aircrafts with low-emissive paint decreases the amount of emitted electromagnetic energy radiating in the infrared (IR) range from the surfaces, making the surfaces appear cooler and thus less detectable by IR sensors. However, paint may be rather soft and require protective coating when applied to surfaces, which are subject to weathering and rain erosion. A protective coating such as a clear coat might interfere with the IR properties of a low-emissive paint, making it less efficient. We have investigated the possibility to add graphene to the paint formulation in order to increase the mechanical durability. Low-emissive paints doped with either graphene or graphene oxide (GO) of lower quality (nano-graphite) and of higher quality were tested with regard to IR reflectance and mechanical durability. IR reflectance was measured by FTIR spectroscopy and the mechanical durability by sclerometer hardness and cross cut adhesion tests. Graphene and GO of lower quality appeared to increase the mechanical durability of the paint while increasing the IR emissivity. Graphene and GO of higher quality also appeared to increase the mechanical durability of the paint. While some of the samples of higher quality increased the IR emissivity, a few samples showed potential to decrease the emissivity.

Important properties for camouflage materials can be summarized in six criteria: (1) spectrally selective reflectance, (2) low gloss, (3) low degree of polarization, (4) low infrared emissivity, (5) non-destructive effect on radar properties and (6) color adaptivity. We have studied a collection of natural materials for potential use as camouflage surfaces for the arctic region. The four first camouflage criteria are analyzed using spectrometry, scatterometry and spectroscopic ellipsometry techniques. The materials involved in the study are diffuse white nature-inspired surfaces: Cuticles of the beetle Cyphochilus insulanus, and foams of freeze-casted cellulose nanofibrils.

We present data that partly fulfills the addressed camouflage criteria. An adequate reflectance is achieved in the spectral range of 400–1600 nm for both samples. Scattering data show that near-Lambertian properties are achieved at 633 nm for both surfaces but at 1550 nm for only the beetle cuticle. The degree of polarization is low for unpolarized light incident near the surface normal for both surfaces.