This PDF file contains the front matter associated with SPIE Proceedings Volume 11536, including the Title Page, Copyright information and Table of Contents.

Hello and welcome to SPIE volume 11536 Target and Background Signatures IV.

In military operations signature reduction techniques such as camouflage nets, low-emissive paints, and camouflage patterns are typically deployed to optimize the survivability of high value assets by minimizing their detectability. Various methods have been developed to assess the effectiveness of these camouflage measures. There are three main approaches to the evaluation of camouflage measures: (1) a subjective approach through observer experiments, (2) an objective computational approach through image analysis, and (3) an objective approach through physical measurements. Although subjective evaluation methods have a direct relation with the operational practice, they are often difficult to implement because of time and budget restrictions, or simply because the associated conditions are not safe for the observers. Objective evaluation methods are typically based on the outcome of psychophysical laboratory experiments using simple artificial stimuli, presented under extremely restricted (impoverished) conditions, and in different experimental paradigms. Objective methods based on signal processing techniques have no obvious counterpart in human vision. So far, no attempts have been made to validate any of these objective metrics against the performance of human observers in realistic military scenarios. As a result, there are currently no standard and internationally accepted methods and procedures to evaluate camouflage equipment and techniques, and to indicate their military effectiveness. In this review paper we present an overview of the various subjective (psychophysical) and objective (computational, image or video based) evaluation methods that are currently available and that have been used to validate camouflage effectiveness. In addition, we will discuss the relative merits of field experiments versus laboratory experiments.

Photosimulation is a widely used method for target detection experimentation. In the defence context, such experiments are often developed in order to derive measures for the effectiveness of camouflage techniques in the field. This assumes that there is a strong link between photosimulation performance and field performance which may hold for situations where the target and background are relatively stationary, such as in land environments. However, there has been some research to suggest that this assumption fails in maritime environments where both the target and background are moving, results implying that the dynamic nature of the search task led to many more cues in field observation compared to still image presented on a screen. In this paper, we explore the link between field observations and photosimulation and videosimulation. Two field observation trials were conducted, at different locations (Flinders and Darwin) and with different, but similarly sized small maritime craft. The small maritime craft deployed in the Flinders field trial in an open ocean environment was harder to detect in photosimulation than in the field. In contrast, the two small maritime craft deployed in the Darwin field trial in a littoral or coastal environment were easier to detect in videosimulation than in the field.

Targets that are well camouflaged under static conditions are often easily detected as soon as they start moving. We investigated and evaluated ways to design camouflage that dynamically adapts to the background and conceals the target while taking the variation in potential viewing directions into account. In a human observer experiment recorded imagery was used to simulate moving (either walking or running) and static soldiers, equipped with different types of camouflage patterns and viewed from different directions. Participants were instructed to search for the soldier and to make a speeded response as soon as they detected the soldier. Mean correct search times and mean detection probability were compared between soldiers in standard (Netherlands) Woodland uniform, in static camouflage (adapted to the local background) and in dynamically adapting camouflage. We investigated the effects of background type and variability on detection performance by varying the soldiers’ environment (like bushland, and urban). In general, performance was worse for dynamic soldiers compared to static soldiers, confirming the notion that motion breaks camouflage. Furthermore, camouflage performance of the static adaptive camouflage condition was generally much better than for the standard Woodland camouflage. Also, camouflage performance was found to depend on the background. When moving across a highly variable (heterogenous) background, dynamic camouflage turned out to be especially beneficial (i.e., performance was better in a bush environment than in an urban environment). Interestingly, our dynamic camouflage design was outperformed a method which simply displays the ‘exact’ background on the camouflage suit, since it is better capable of taking the variability in viewing directions into account. By combining new adaptive camouflage technologies with dynamic adaptive camouflage designs such as the one presented here it may become possible to prevent detection of moving targets in the (near) future.

The goal of this experimental research is to determine the potential of pure acoustic detection for hostile fire indication for helicopters, while guarantying a very low true false alarm rate in the case of high helicopter speed, large helicopter bullet distance range and small calibres. An integration into a multi-sensor system, could provide a fully embedded, defence-feature technology capable of immediately alerting a helicopter crew that even a small projectile has closely passed by their machine, and provide an indication of the bullet trajectory. This article summarizes the design, the test in real-flight situation, and the performance results of an experimental acoustic array demonstrator developed for this purpose and evaluated for a referential of situations of 4 small arms firings with calibres from 5.56 mm to 12.7 mm and a total of 592 firings  52 flight exposure situations (altitude, hover, speed, orientation). The detection system is an experimental planar, centimetric, acoustic array recording unit designed for low sensitivity to laminar and turbulent air flow, rear pressure and vibrations and high acoustic selectivity. This is combined with a transient signal post-processing solver that is implemented as a deterministic cost minimization function. The conclusion of the research is a strong confirmation of the feasibility of an under-helicopter acoustic detection of small calibres hostile firings as well as the proposal of a general performance law for the capability of such a system to detect small calibres, including passing-by distances of up to several hundreds of m and speeds of up to 120 kn.

With improved specifications and capabilities of modern sensors and detectors, concealment is an increasingly challenging endeavor. Concealment from modern sensors requires advanced camouflage material that can provide low background contrast over a wide range of spectral wavelengths. Multi-layer material (i.e. textile fibers) allow for advantageous camouflage abilities such as improved heat transfer and modification of spectral signatures. In this study, we investigate the effect of multiple layers on the reflectance properties of a camouflage net. Camouflage nets provide protection against visual, thermal and radar threats, and can be tailored to offer effective concealment in various natural backgrounds and climate zones. By utilizing a simple mathematic model, we predict multi-layered reflectance properties of the camouflage net from single-layer reflectance data. The model is in good agreement with measurement data for multi-layer net material, and the materials in the study behaves similar to partly transmitting leaves. We also find that 2-3 layers of the materials is sufficient to hinder reflectance contributions from the background. At certain wavelengths, the required number of layers is even lower and reveals that the transmission and reflectance are wavelength dependent.

We present an alternative measurement method for BRDF characterization. It is based on imaging instead of angular sampling. Under illumination, the reflectance characteristic of the analyzed material is projected onto a hemispheric diffuse reflective dome surrounding the probe. Images of the dome are taken to capture the directional distribution of the reflected light. This shows the main advantage of the new method: The simultaneous capture of the reflectance within a hemispherical sector, therefore accelerating greatly the data acquisition. However, some additional processing steps have to be implemented to achieve results comparable to the sampling method. Captures with different integration times have to be merged into a high dynamic range (HDR) image and a pixel mapping to absolute scattering angles in spherical coordinates (elevation and azimuth) has to be registered. Measurements acquired with this fist simple approach are quantitatively compared to the aforementioned established sampling acquisition method for two different materials: A diffuse Lambertian reference and a higher gloss degree material. These results are discussed in the last section and will serve as guidelines for future iteration developments of the proposed method.

In this work, we studied how detection range of a small target changes as a function of the number of defined target pixels. The intuitively simplest method to look for a target in IR images is to look for the “hottest” (highest intensity) pixel. Image noise or sun glint may cause false detection. To build in some robustness against such detractions, one considers looking for the hottest group (blob) of contiguous pixels. A blob could be any shape; here we consider square blobs, 1x1, 2x2, 3x3, etc. pixels. One expects the average blob intensity to decrease with blob size. On the other hand, the noisiness of the background blobs also decreases with blob size. The net result is that initially, for small blob sizes, the detection range increases before falling again for larger blob sizes. We demonstrate this by analyzing IR recordings of a small vessel sailing outbound until it “disappears”. We develop a simple model that supports these observations. The model is based on a synthetically generated sequence of images of a receding target, uses a basic sensor characteristic, and Johnson’s detection criterion.

Rapid advancements in EO/IR imaging technology boosted by developments in focal plane array technology led to significant increase in performance, availability and accordingly in application in the various systems. As a consequence the more efforts in the area of possible countermeasure development are necessary. Starting from EO/IR generalized image forming process and related influences on the imager performances as key part of imager performance, using knowledge generated from well-established electronic countermeasure science and known results in EO/IR countermeasure application, EO/IR countermeasures classification is proposed. Using this classification the currently known countermeasures are analyzed trying to identify existing challenges for future efforts. Also the recent advanced in image processing techniques, i.e. application of artificial intelligence for automatic target recognition, could be used as EO/IR imaging counter-counter measure and should be considered separately.

Performance assessment of image processing systems is typically carried out using large volumes of data with known ground truth. Unfortunately such data can be challenging to source for many problems of interest. In particular, trials data collection for performance assessment may require the acquisition of imagery for a range of sensing, target and environmental conditions. This can be time consuming and expensive to achieve. We might also wish to assess the performance of systems which are yet to be built, or over target objects for which access is, at best, limited. These problems might be addressed through the use of synthetically-generated imagery which may be produced in volume for the sensor, environment and target configurations required. If sufficiently representative these may then be used within the performance assessment process for system characterization. A scheme for the generation of synthetic imagery has previously been published. This is deemed fit-for-purpose for algorithm and systems performance assessment of image processing for Automatic Target Detection and Recognition (ATDR) tasks in Synthetic Aperture Radar (SAR) imagery. The approach is effective up to the intermediate resolutions which are typically used for ATDR applications. The input models used for synthesis are comprised of three-dimensional triangulations representing the geometric structure of the scene content, with each triangle having a parameterized scattering response based on SAR distributional models. The modelling process is reliant on a skilled analyst to identify and encode the salient detail of the target object including: the geometry of the principal structural features, and; by encoding the surface textures within the scattering response. The synthesis process generates a collection of two-dimensional arrays of distributional parameters the same size as the image to be produced. It is straightforward to use these to generate representations of expected scattering response, or realistic - looking simulated SAR images with speckle. This paper examines the development of target models for use within the simulator, along with a selection of performance assessment results produced for a range of sensor characteristics.

The three-dimensional noise model is a methodology to analyse the noise of a thermal imaging sensor, such as an infrared (IR) camera. This allows us to decompose a noisy signal into components and quantify properties such as noise equivalent temperature difference (NETD), temporal noise, rain, streaks, or various types of fixed pattern noise. As part of this analysis, it is necessary to identify trends in order to split the data into signal and noise. In this paper we discuss methods to perform this split. We then show that not only the noise, but also the trends contain interesting information and can be used to quantify large-scale non-uniformities in calibrated IR images. We apply this analysis to investigate three different effects that may appear in recorded data: How does the uniformity of the background change when we vary the temperature, the distance, or the lens focus? We have performed a series of laboratory measurements on blackbodies in order to investigate these effects. We find that large-scale non-uniformity may be present even in calibrated images, with an order of magnitude up to ΔT~0:6 K.

We applied a simple method to estimate the Minimum Resolvable Temperature Difference (MRTD) of an LWIR and an MWIR camera. A so-called Siemens star, in our case a thin, black aluminum plate framing a circle that is missing (cut out) every other spoke, is mounted in front of a black body whose temperature is relatively close to room temperature. From short recordings of the black body and Siemens star both the Noise Equivalent Temperature Difference (NETD) and the Modulation Transfer Function (MTF) are extracted and a simple estimate of MRTD = NETD/MTF is obtained. The imaged Siemens star almost completely covers the focal plane array; hence, an MRTD curve for the whole array is obtained. We investigated the effect of Non-Uniformity Correction (NUC) and Bad-Pixel Removal (BPX), two often applied pre-processing techniques, on the MRTD estimate. We find that (1) BPX has only limited effect on the result; (2) NUC is required to obtain a good MTF; and (3) NUC is not a prerequisite to obtain a good NETD estimate, but this is contingent on having a proper segmentation tool or template available. Without a segmentation algorithm, NUC together with simple intensity thresholding provides a sufficiently good segmentation and accordingly a good estimate of NETD.