A graphical user interface for multi-modal operating condition sampling

Automatic target recognition (ATR) is a challenging task for several computer vision applications. It requires efficient, accurate, and robust methods for target detection and target identification. Deep learning has shown great success in many computer vision applications involving color RGB images. However, the performance of these networks in ATR with infrared sensor data needs further investigation. In this paper, we propose a multistage automatic target detection and recognition (ATDR) system that performs both target detection and target classification on infrared (IR) imagery using deep learning. Our system processes large IR image frames where targets take <1 % of the total number of pixels. First, we train a state-of-the-art object detector you only look once (YOLO) to localize all potential targets in the input image frame. Then, we train a convolutional neural network (CNN) to identify these detections as targets or false alarms. In this second phase, we adapt and analyze the performance of three CNN architectures: a compact and fully connected CNN, VGG16 with batch normalization, and a wide residual neural network (WRN). We also explore the use of a loss function that optimizes directly the area under the receiver operating characteristic (ROC) curve (AUC), and adapt it to our ATR application. To enhance the robustness of the proposed ATR to perturbation and variations introduced during the detection stage, we train our CNN classifiers on automatically detected targets using YOLO, in addition to ground truth bounding boxes and apply selected data augmentation techniques. To simulate real testing environments, where the spatial location of the targets within the image frame is unknown, only YOLO-detected boxes are used during validation. We evaluate our ATDR on a real benchmark dataset that includes different vehicles captured at different resolutions. Our experiments have shown that YOLO can detect most of the targets at the expense of generating a high number of false alarms. We show that the VGG-16 network with batch normalization, which is the best performing model, can correctly identify the classes of the targets, as well as classify the majority of YOLO’s false detections into an additional nontarget class. We also show that the proposed training modification to optimize an AUC-based loss function for ATR proved to be advantageous mainly in identifying difficult targets.

This paper deals with several original contributions to an automatic target recognition (ATR) system, which is applied to underwater mine classification. The contributions concentrate on feature selection and object classification. First, a sophisticated filter method is designed for the feature selection. This filter method utilizes a novel feature relevance measure, the composite relevance measure (CRM). Feature relevance measures in the literature (e.g., mutual information and relief weight) evaluate the features only with respect to certain aspects. The CRM is a combination of several measures so that it is able to provide a more comprehensive assessment of the features. Both linear and nonlinear combinations of these measures are taken into account. A wide range of classifiers is able to provide satisfactory classification results by using the features selected according to the CRM. Second, in the step of object classification, an ensemble learning scheme in the framework of the Dempster-Shafer theory is introduced to fuse the results obtained by different classifiers. This fusion can improve the classification performance. We propose a reasonable construction of the basic belief assignment (BBA). The BBA considers both the reliability of the classifiers and the support of individual classifiers provided to the hypotheses about the types of test objects. Finally, this ATR system is applied to real synthetic aperture sonar imagery to evaluate its performance.

The use of robust and representative synthetic imagery data to test and evaluate automatic target recognition (ATR) systems has long been desired but generally considered beyond the current state of the art. The use of synthetic data is investigated here to calculate upper bounds on potential ATR system performance. This paper presents the use of synthetically generated imagery templates as a means of developing upper bounds of ATR performance for laser radar based seekers. This approach employs a synthetic scene generation capability and integrates it with error models that represent decrements in performance due to resolution, noise and geometric distortion resulting from the sensing process. This paper describes the modeling approach take and presents preliminary results. The model is currently undergoing testing against real imagery and is being used to select test sets to more effectively evaluate ATR's.

Performance of automatic target recognition (ATR) systems depends on numerous factors including the mission description, operating conditions, sensor modality, and ATR algorithm itself. Performance prediction models sensitive to these factors could be applied to ATR algorithm design, mission planning, sensor resource management, and data collection design for algorithm verification. Ideally, such a model would return measures of performance (MOPs) such as probability of detection (Pd), correct classification (Pc), and false alarm (Pfa), all as a function of the relevant predictor variables. Here we discuss the challenges of model-based and data-based approaches to performance prediction, concentrating especially on the synthetic aperture radar (SAR) modality. Our principal conclusion for model-based performance models (predictive models derived from fundamental physics- and statistics-based considerations) is that analytical progress can be made for performance of ATR system components, but that performance prediction for an entire ATR system under realistic conditions will likely require the combined use of Monte Carlo simulations, analytical development, and careful comparison to MOPs from real experiments. The latter are valuable for their high-fidelity, but have a limited range of applicability. Our principal conclusion for data-based performance models (that fit empirically derived MOPs) offer a potentially important means for extending the utility of empirical results. However, great care must be taken in their construction due to the necessarily sparse sampling of operating conditions, the high-dimensionality of the input space, and the diverse character of the predictor variables. Also the applicability of such models for extrapolation is an open question.

Proper evaluation of a pattern recognition system in the lab is paramount to its success in the field. In most commercial pattern recognition applications, such as breast cancer detection, optical character recognition, and industrial quality assurance, the boundaries and expectation of the system are well defined. This is due, at least in part, to an excellent understanding of the problem and data space for these applications. For these functions, a method for rigorous evaluation is well understood. However, the size and complexity of the data and problem spaces for automatic target recognition (ATR) systems is enormous. The consequences are that a complete understanding of how an ATR system will perform in practice is extraordinarily difficult to estimate. Thus the act of evaluating an ATR system becomes as important as its design. This paper compiles and reports the techniques used to evaluate ATR system performance. It surveys the specific difficulties associated with ATR performance estimation as well as approaches used to mitigate these obstacles.

The resolution capability of imaging systems is effected by a blurring effect due to the vibration and motion recorded in the image. This disturbance is often more severe than electronic and optical limitations inherent in the system. This fact must be considered when dealing with the development and analysis of automatic target recognition (ATR) systems for military applications. The aim of this research is to analyze the influence of image vibrations and motion upon the probability of acquiring the target with an ATR system. The analysis includes factors that characterize the relationship existing between the target and its background. A high level of correlation is expected between these factors and the probability of target detection, enabling efficient performance in the prediction and evaluation of any ATR system. The results of this research can be implemented in military applications as well as in developing image restoration procedures for image-blur conditions.

Low altitude, small radar cross-section (RCS), and slow speed (LSS) targets, for example small unmanned aerial vehicles (UAVs), have become increasingly significant. In this paper, we propose a new automatic target recognition (ATR) system and a complete ATR chain based on multi-dimensional features and multi-layer classifier system using L-band holographic staring radar. We consider all steps of the processing required to make a classification decision out of the raw radar data, mainly including preprocessing for the raw measured Doppler data including regularization and main frequency alignment, selection, and extraction of effective features in three dimensions of RCS, micro-Doppler, and motion, and multi-layer classifier system design. We design creatively a multi-layer classifier system based on directed acyclic graph. Helicopters, small fixed-wing, and rotary-wing UAVs, as well as birds are considered for classification, and the measured data collected by L-band radar demonstrates the effectiveness of the proposed complete ATR classification system. The results show that the ATR classification system based on multi-dimensional features and k-nearest neighbors (KNN) classifier is the best, compared with support vector machine (SVM) and back propagation (BP) neural networks, providing the capability of correct classification with a probability of around 97.62%.

Combining the capability of an automated target recognition (ATR) system with the expertise of human operators has the potential to improve combined human-machine target-recognition performance. The objective of this work was to develop empirically validated guidelines for the display of information provided by an ATR system. Motivated by the premise that operators will have higher trust in an ATR system that justifies its decisions, this work explored alternative ATR displays that provided different types and amounts of information in support of the system's target decisions. Operators' usage of and preference for the displays were contrasted for low- and high-accuracy simulated ATR systems representative, respectively, of current and future technologies. Performance results suggested that the amount of supporting information that is displayed must be congruent with the ATR system's accuracy. Operators expressed a clear preference for an ATR system that provides a confidence rating for its target decisions. Based on the results design guidelines are suggested for displaying ATR information to help operators balance confidence in their own judgments with trust in the ATR's decisions.

: This report focuses on the development of an automatic target recognition (ATR) system using high resolution synthetic aperture radar (SAR) imagery. The system achieves 95 to 100 percent recognition rates when applied to a set of MSTAR images. Typically, the system takes less than one minute to match an input image to a candidate vehicle class with Matlab programs running on a Pentium II 300 MHz machine. Experiments based on conventional recognition techniques were conducted for comparisons. Study of persistent scattering confirms the feasibility of implementing a SAR ATR system using physical image features. A new generic vehicle model, parameterized by the length, width, and orientation of a target is used in a two-phase recognition process with hypothesis generation and verification aimed at addressing the combinatorial target recognition problem. In the hypothesis generation stage, a few likely candidate target classes are identified from a target database with positive evidence. The candidates are assessed using both positive and negative evidence in the hypothesis verification stage. Leading surface estimation, image alignment, Delaunay walk, and recognition metrics are introduced to improve performance of the SAR ATR system.

We present a performance analysis of an indexing module contained in an automatic target recognition (ATR) system developed at Worcester Polytechnic Institute (WPI). The linear signal decomposition/direction of arrival (LSD/DOA) technique provides a low computational-cost indexing function for pose in ATR applications. The LSD/DOA technique forms a reduced-size, essential-information object data set which implicitly incorporates target and sensor variation specific data. To control the computational costs, the system consists of two independent components: a computationally expensive off-line component which forms the object representation and a computationally inexpensive on-line object recognition component. The size of the stored data set may also be adjusted providing a means to trade off complexity versus performance. Synthetic aperture radar (SAR) data collected as part of the Moving and Stationary Target Acquisition and Recognition (MSTAR) program has been released to the public, providing an opportunity for ATR performance assessment against a standard high quality data set. We present the results of a number of simulated tests against the MSTAR public database to demonstrate the performance of the LSD/DOA index module on a given target along with the performance of the WPI ATR itself as a function of the depression angle and target configuration.

Forward Looking Infrared (FLIR) automatic target recognition (ATR) systems depend upon the capacity of the atmosphere to propagate thermal radiation over long distances. To date, not much research has been conducted on analyzing and mitigating the effects of the atmosphere on FLIR ATR performance, even though the atmosphere is often the limiting factor in long-range target detection and recognition. The atmosphere can also cause frame-to-frame inconsistencies in the scene, affecting the ability to detect and track moving targets. When image quality is limited by turbulence, increasing the aperture size or improving the focal plane array cannot improve ATR performance. Traditional single frame image enhancement does not solve the problem. A new approach is described for reducing the effects of turbulence. It is implemented under a lucky-region-imaging framework using short integration time and spatial domain processing. It is designed to preserve important target and scene structure. Unlike previous Fourier-based approaches originating from the astronomical community, this new approach is intended for real-time processing from a moving platform, with ground as the background. The system produces a video stream with minimal delay. A new video quality measure (VQM_turb) is presented for quantifying the success of turbulence mitigation on real data where no reference imagery is available. The VQM_turb is the core of the innovation because it allows a wide range of algorithms to be quantitatively compared. An algorithm can be chosen, and then tuned, to best-fit available processing power, latency requirements, scenarios and sensor characteristics.

In support of the ARPA-sponsored Critical Mobile Targets program, Lincoln Laboratory has developed a complete, end-to-end, automatic target recognition (ATR) system using very high resolution (1 ft/spl times/1 ft), fully polarimetric, Ka-band SAR data. Lincoln Laboratory has also developed ATR systems for reduced resolution (1 m/spl times/1 m) and single polarization data; performance comparisons (1 ft/spl times/1 ft versus 1 m/spl times/1 m and fully polarimetric versus single polarization) have been performed. There has been interest in the use of automatic target cueing (ATC) followed by ATR. In military operations, targets are often deployed in well-defined groups, such as tank companies. It is possible to exploit this information by developing algorithms to detect such groups of targets. Cueing algorithms identify potential target groups; then these areas can be revisited with a higher resolution sensor or these areas can be processed with more sophisticated algorithms (or both). This paper presents results from a joint ATC/ATR system that uses single polarization (HH) 1 m/spl times/1 m resolution data for both ATC and ATR; descriptions of the algorithms in the system are given and the performance is compared to the (1 ft/spl times/1 ft) very accurate resolution system run on the identical data set. The data used in this study consisted of 231 km/sup 2/ of 20 degree depression angle stripmap clutter from several areas in the northern United States; these data had varying amounts of cultural content from benign to severe.

Biologically motivated cross-modality sensory fusion system for automatic target recognition

To quantify the agility of a synthetic aperture radar (SAR) system using an automatic target recognition (ATR) system to detect targets obscured by foliage, an ultra-wideband, UHF- band, polarimetric SAR was constructed by ERIM under ARPA funding and installed on a Navy P-3 aircraft controlled by the Naval Air Warfare Center. The system was implemented as an upgrade to the existing X-, L-, and C-band SAR system already on this aircraft. A series of experiments funded by ARPA and Wright Laboratory were undertaken in 1995 to investigate foliage penetration (FOPEN). In this paper, the data and ground truth collected and their utility for investigations of FOPEN phenomenology and ATR algorithms will be presented. These data are being placed into a database for distribution to ATR algorithm developers. The characteristics of the P-3 UWB SAR will be discussed. The image formation technique used will be presented, along with the RFI suppression techniques used. Of particular interest will be the technique used for the required motion compensation. Results from recent investigations using the P-3 UWB SAR data will be discussed.

일반적인 ATR시스템에서는 대부분 FLIR센서에 의존하여 영상을 획득하나, 표적의 경계가 모호한 경우 견실한 표적 분할을 보장할 수 없는 한계점이 있다. 이에 본 논문은 FLIR과 CCD센서를 통해 획득된 영상에 대한 적응적 가중치 기반의 융합 방법을 제안함으로써 보다 정확한 표적 분할 성능을 재현한다. 융합을 위한 FLIR영상의 가중치는 모호한 경계를 구분하기 위한 bi-modality 척도와 표적 경계와의 거리를 통해 결정되고, CCD영상의 가중치는 표적과 배경의 질감차이를 나타내는 질감 척도와 거리 척도를 통해 도출된다. 제안 방법의 타당성 검증을 위하여 다양한 환경에서 획득된 표적 영상에 대한 제안 방법과 단일 센서 기반의 표적 분할의 성능 비교를 수행하였다. In automatic target recognition(ATR) systems, target extraction techniques are very important because ATR performance depends on segmentation result. So, this paper proposes a multi-sensor image fusion method based on adaptive weights. To incorporate the FLIR image and CCD image, we used information such as the bi-modality, distance and texture. A weight of the FLIR image is derived from the bi-modality and distance measure. For the weight of CCD image, the information that the target's texture is more uniform than the background region is used. The proposed algorithm is applied to many images and its performance is compared with the segmentation result using the single image. Experimental results show that the proposed method has the accurate extraction performance.