Pose estimation of unresolved targets using polarimetric imaging

Following the tendency of increasingly using imaging sensors in military aircraft, future combat airplane pilots will need onboard artificial intelligence for aiding them in image interpretation and target designation. This document presents a system which is able to simulate high-resolution artificial SAR imagery and thereby facilitates automatic target recognition (ATR) algorithm development. The system provides a comprehensive interface that allows dynamically requesting imagery depending on the location and heading of a simulated carrier platform. Landscapes, structures and target signatures are generated based on digital terrain data and target models. An assessment of dissimilar database preparations for sensor simulation was done with respect to the different properties of SAR imaging compared to optical imaging. The document presents selected results for specific landscape elements. Post-processing algorithms for overcoming weaknesses of digital terrain databases and improving image realism are presented. Simulated sensor imagery is useful in a wide range of applications, two of which are training of ATR algorithms and sensor simulation in flight simulation environments. Using an existing ATR method as an example, the applicability and the influences of synthetic imagery on ATR training are shown and first approaches on how to validate the correctness of the imagery are explained. The integration of the system into a flight simulator in the context of interfacing and control topics serves as a concluding example.

The MACH and DCCF correlation filter algorithms are evaluated using the publicly released MSTAR data base. These algorithms can be used as a matching engine for automatic target recognition in SAR imagery. In practice, the required filters can be synthesized using model based signature predictions. In addition, the MACH and DCCF algorithms are optimized to be robust to variations (distortions) in the target's signature. Unlike Matched Filtering or other exhaustive template based methods, the proposed approach requires very few filters. The paper describes the theory of the algorithm, key practical advantages and details of test results on the public MSTAR data base.

The ability of participants to simulate the width, height, and width: height ratio of simple and complex target signatures has been explored. The natural variation of the target signatures was evaluated over 29 and 28 signatures for the simple and complex signatures respectively. A group of 20 participants were provided 12 simulations of each signature on lined and plain paper. Paired t-tests show that the complex signature was more consistently reproduced than the simple signature. Single factor Analysis of Variance (ANOVA) followed by post hoc Tukey Honestly Significant Difference (HSD) tests showed that the complex signature was more easily simulated.

The problem of detecting buried objects has engaged radar system developers for quite some time. Many systems—both experimental and commercial—have been developed, including vehicle-mounted systems that look beneath road surfaces. Most of these downward-looking systems exploit multiple transmit and receive channels to enhance resolution in the final radar imagery used for target detection. In such a system, the configuration and operation of the various transmit and receive elements play a critical role in the quality of the output imagery. In what follows, we leverage high-fidelity electromagnetic model data to examine a multistatic downward-looking radar system. We evaluate the signatures produced by various targets of interest and describe, both qualitatively and quantitatively, the variations in target signatures produced by different system configurations. Finally, we analyze the underlying physics of the problem to explain certain characteristics in the observed target signatures.

On-orbit real-time robust cooperative target identification in complex background

We propose a polarimetric microwave imaging technique that exploits recent advances in computational imaging. We utilize a frequency-diverse cavity-backed metasurface, allowing us to demonstrate high-resolution polarimetric imaging using a single transceiver and frequency sweep over the operational microwave bandwidth. The frequency-diverse metasurface imager greatly simplifies the system architecture compared with active arrays and other conventional microwave imaging approaches. We further develop the theoretical framework for computational polarimetric imaging and validate the approach experimentally using a multi-modal leaky cavity. The scalar approximation for the interaction between the radiated waves and the target- often applied in microwave computational imaging schemes-is thus extended to retrieve the susceptibility tensors, and hence provides additional information about the targets. Computational polarimetry has relevance for existing systems in the field that extract polarimetric imagery, and particular for ground observation. A growing number of short-range microwave imaging applications can also notably benefit from computational polarimetry, particularly for imaging objects that are difficult to reconstruct when assuming scalar estimations.

The Stokes imaging polarimeter using a retarder and a polarizer is a direct and conventional method to detect the full Stokes vector of the scene. This method has been widely used for different imaging architectures, such as division of time and division of aperture. System calibration is necessary in polarimetric imagery. The polarization optical elements angular deviation belongs to adjustment error and should be calibration when the device is set up. In this paper we propose a novel method to calibration the angular deviation using Fourier series analysis. A linear polarization state generator including a light source and a linear polarizer is installed before the imaging polarimeter. The polarizer rotates 360-degree uniform and the signal intensity of the input light is recorded by the imaging polarimeter. The angle of the fast axis of the quarter-waveplate respect to the pass axis of the polarizer can be calculated from the obtained data by use of Fourier series analysis. The mathematic model of the presented method is established and proved by mathematical simulation.

To improve signal detection in a turbid medium, we propose temporally encoded single shot polarimetric integral imaging. An optical signal is temporally encoded using gold coded sequences and transmitted through a turbid medium. The encoded signals are captured as a sequence of elemental images by two orthogonal polarized image sensor arrays. Polarimetric and polarization difference imaging are used to suppress the partially polarized and unpolarized background noise such that only the polarized ballistic signal photons are captured at the sensor. Multidimensional integral imaging is used to obtain 4D reconstructed data, and multidimensional nonlinear correlation is performed on the reconstructed data to detect the optical signal. We compare the effectiveness of the proposed polarimetric underwater optical signal detection approach to conventional (non-polarimetric) integral imaging-based and 2D imaging-based signal detection systems. The underwater signal detection capabilities are measured through performance metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical measures, including the Kullback-Leibler divergence, signal-to-noise ratio (SNR), and peak-to-correlation energy (PCE), are also calculated to show the improved performance of the proposed system. Our experimental results show that the proposed polarimetric integral-imaging approach significantly outperforms the conventional imaging-based methods. To the best of our knowledge, this is the first report on temporally encoded single shot polarimetric integral imaging for signal detection in turbid water.

Polarimetric sensing provides additional scene information which can be used to enhance the target detection and recognition performance of an imaging system. Such information is particularly valuable in the discrimination of weak target signatures from their surroundings and, as such, is attracting a growing interest for future military and surveillance applications. However, the extraction of polarisation information adds complexity in terms of the physical sensor design and the required data processing. Imaging polarimeters typically require four images to be captured of the same scene in different polarisation orientations through either time or spatial division techniques. These sensor architectures introduce system performance constraints in terms of temporal and spatial resolution as well as the attendant degradations associated with the use of additional optical components. Issues associated with the physical envelope, operational robustness, and cost must also be considered. In terms of the processing of the polarimetric data, accurate registration and calibration is required to extract small polarisation signatures which are typically found in natural scenes. The polarimetric image data must then be processed using an image fusion or data fusion method which introduces further demands on the software design and system processor. For some applications, these limitations are acceptable relative to the polarimetric gain, whilst in others a conventional imaging sensor may offer a better overall solution. Consequently, it is important that a trade-off analysis is undertaken which evaluates the realistic performance gains with respect to the implementation and cost issues.

Manmade objects typically exhibit polarized signatures that can be utilized for target detection more effectively than thermal signatures alone. IR polarimetric imaging can also suppress clutter to highlight manmade objects in thermal equilibrium. However, quantifying the contrast improvement of polarimetric signatures over that of thermal signatures of the same scene has proved problematic due to the nature and dynamic range of the data. The Pyxis camera, a microbolometer-based imaging polarimeter that produces live polarimetric video of conventional, polarimetric, and fused image products and is small enough to be mounted on commercial drones, has collected datasets that demonstrate the improvement. In this paper, we describe the Pyxis camera, show representative data, and present the metrics used to characterize the improvement of polarimetric imaging over that of conventional thermal imaging.

The implementation of automatic seat belt monitoring systems contributes to improving road safety by reducing the risks associated with road accidents. Achieving a good-performance detection system requires a solution that makes the seat belt stand out from its surrounding environment. The results of our study demonstrate that seat belts exhibit an optical polarimetric signature that differentiates them from other common textiles used in clothing. Most fabrics behave as mostly isotropic depolarizers, reducing the degree of polarization of light independently of its initial polarization state. In contrast, seat belts selectively depolarize polarized light-maintaining a higher degree of polarization when the light is linearly polarized either parallel or perpendicular to the belt axis. Our findings suggest that carefully selecting the polarization state of the illumination can enhance the separability between the seat belt and other types of fabrics, which could ultimately improve the effectiveness of automatic seat belt detection systems.

Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.

In a low signal-to-clutter ratio (SCR) small-infrared-target image with chaotic cloudy-/sea-sky background, the target has very similar thermal intensities to the background (e.g., edges of clouds). In such case, how to accurately detect small targets is crucial in infrared search and tracking applications. Conventional methods based on the local difference/mutation potentially result in high miss and/or false alarm rates. Here, we propose an effective method for detecting small infrared targets embedded in complex backgrounds through a multiscale fuzzy metric that measures the certainty of targets in images. Accordingly, the detection task is formulated as a fuzzy measure issue. The presented metric is able to eliminate substantial background clutters and noise. Especially, it significantly improves SCR values of the image. Subsequently, a simple and adaptive threshold is used to segment target. Extensive clipped and real data experiments demonstrate that the proposed algorithm not only works more robustly for different target sizes, SCR values, target and/or background types, but also has better performance regarding detection accuracy, when compared with traditional baseline methods. Moreover, the mathematical proofs are provided for understanding the proposed detection method.

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

Data fusion is a very effective technique which can be applied to many remote sensing areas such as classification, monitoring of environmental surveillance and man-made target tracking. In this paper, we tested fusion of multiple frequency (C-, and L-band), multiple polarization (HH, HV and VV) and multiresolution data sets. One can obtain a polarimetric SAR data after enhancing spatial resolution through the image fusion process. In order to fuse multiple SAR data and high spatial resolution data, they have to be geometrically co-registered over the same target area and have the same pixel size (spatial registrations). At this stage, we used the nearest neighbor resampling to avoid spectral distortion by interpolation. Multiresolution polarimetric SAR image fusion was performed using the multiscale image fusion technique-discrete wavelet transform after spatial registrations. To evaluate the spectral fidelity of fused polarimetric SAR data, spectral dissimilarity was calculated at each wavelet decomposition level. The resulting classification map based on polarimetric feature vectors shows better class separation after application of fusion processing than without fusion. The polarimetric SAR data over the Gong-ju areas, tested in this research, were acquired during NASA/JPL AIRSAR PACRIM-II experiment in 2000.