一种基于高分辨率距离像自动目标识别新方法

提出了一种基于高分辨率距离像的联合对准与识别新方法。该方法结合功率变换的使用，在利用8米雷达目标实测数据进行的识别实验中，获得了较高的正确识别率。     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

Automatic Target Recognition: State of the Art Survey

In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.     

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective     

Performance of 10- and 20-target MSE classifiers

MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target mean square error (MSE) classifiers using high-resolution SAR (synthetic aperture radar) imagery.     

Image understanding research for automatic target recognition

Automatic target recognition (ATR) is an important capability for defense applications. Many aspects of image understanding (IU) research are traditionally used to solve ATR problems. The authors discuss ATR applications and problems in developing real-world ATR systems and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. They conclude that technological gains in developing robust ATR systems will lead to significant advances in many other areas of applications of image understanding     

A prospectus for automatic target recognition

Aspects of autorecognition technology are discussed. The following topics are covered: The context and role of automatic target recognition (ATR), sensors and ATR systems, generic shape discrimination, countermeasures, and current capabilities. The authors conclude that the material supports a very favorable assessment of the power and importance of emerging ATR technology     

一种新的红外目标图像质量评价方法

 目标混淆度（DTC）和目标遮隐度（DTS）是两个有效的红外小目标图像质量评价指标,然而它们不能够评价大目标图像的质量。针对该问题,对两个指标的定义进行了拓展,实现了对红外小目标图像质量和红外大目标图像质量的统一描述。对于小目标图像,修正了两个指标原有的计算方法,分别体现了背景噪声引入虚警的能力和背景区域遮隐目标的能力;对于大目标图像,提出了该图像条件下两个指标的计算方法,分别体现了背景区域与目标区域的相似程度以及图像中目标信息与已知信息相比偏离的程度。理论分析和实验证明,与传统的图像质量评价标准如信噪比、杂波尺度等相比,本文方法在红外自动目标识别中的应用更为有效,其评价结果与实际情况更加吻合。     

Improved SAR target detection via extended fractal features

The utility of the extended fractal (EF) feature is evaluated for the enhancement of the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. Unlike more traditional SAR detection features that distinguish target pixels from the background only on the basis of contrast, the EF feature is sensitive to both the contrast and size of objects. Furthermore, the structure for the EF feature computational algorithm lends itself to very fast implementation, and it can be shown that the new feature has a CFAR-like (constant false alarm rate) property. We demonstrate the improved performance using the new feature by testing a number of different detection approaches over two databases of SAR imagery     

MMWR/FLIR/ATR sensor fusion: proof of concept

To improve the relocatable target capabilities of strategic aircraft, a sensor fusion concept using a millimeter-wave radar (MMWR) and a forward-looking infrared (FLIR) system providing inputs to an auto target recognizer (ATR) has been developed. To prove this concept, a cooperative research effort is being conducted by a group of industry leaders in bomber avionics, MMWR, and ATR technologies. The author discusses the concept and the plan developed to test, evaluate, and demonstrate the expected performance     

Adaptive boosting for SAR automatic target recognition

The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature     

一种基于红外图像的目标自动识别算法

红外成像制导具有在各种复杂战术环境下自主搜索、捕获、识别和跟踪目标的能力,代表了当代红外制导技术的发展趋势。提出了一种红外图像预处理、跟踪、分类的自动目标识别算法,利用小波变换、形态学方法对红外图像进行预处理,提取不同频带的惯性不变矩作为特征量,利用神经网络进行分类识别,结果表明该算法具有很高的识别率,对于精确制导武器的目标识别研究具有一定的参考价值。     

Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging

We present an evaluation of the impact of a recently proposed synthetic aperture radar (SAR) imaging technique on feature enhancement and automatic target recognition (ATR) performance. This image formation technique is based on nonquadratic optimization, and the images it produces appear to exhibit enhanced features. We quantify such feature enhancement through a number of criteria. The findings of our analysis indicate that the new feature-enhanced SAR image formation method provides images with higher resolution of scatterers, and better separability of different regions as compared with conventional SAR images. We also provide an ATR-based evaluation. We run recognition experiments using conventional and feature-enhanced SAR images of military targets, with three different classifiers. The first classifier is template based. The second classifier makes a decision through a likelihood test, based on Gaussian models for reflectivities. The third classifier is based on extracted locations of the dominant target scatterers. The experimental results demonstrate that the new feature-enhanced SAR imaging method can improve the recognition performance, especially in scenarios involving reduced data quality or quantity.     

Performance complexity study of several approaches to automatictarget recognition from SAR images

A framework which allows for the direct comparison of alternate approaches to automatic target recognition (ATR) from synthetic aperture radar (SAR) images is described and applied to variants of several ATR algorithms. This framework allows comparisons to be made on an even footing while minimizing the impact of implementation details and accounts for variation in image sizes, in angular resolution, and in the sizes of orientation windows used for training. Alternate approaches to ATR are characterized in terms of the best achievable performance as a function of the complexity of the model parameter database. Several approaches to ATR from SAR images are described and the performance achievable by each for a range of database complexities is studied and compared. These approaches are based on a likelihood test under a conditionally Gaussian model, log-magnitude least squared error, and quarter power least squared error. All approaches are evaluated for a wide range of parameterizations and the dependence on these parameters of both the resulting performance and the resulting database complexity is explored. Databases for all of the approaches are trained using identical sets of images and their performance is assessed under identical testing scenarios in terms of probability of correct classification, confusion matrices, and orientation estimation error. The results indicate that the conditionally Gaussian approach outperforms the other two approaches on average for both target recognition and orientation estimation, that accounting for radar power fluctuation improves performance for all three methods, and that the conditionally Gaussian approach normalized for power delivers average performance that is equal or superior to all other considered approaches     

Gabor Filter Approach to Joint Feature Extraction and Target Recognition

This paper presents a new approach of improving automatic target recognition (ATR) performance by tuning adaptively the Gabor filter. The Gabor filter adopts the network structure of two layers, and its input layer constitutes the adaptive nonlinear feature extraction part, whereas the weights between output layer and input layer constitute the linear classifier. From the statistic property of high-resolution range profile (HRRP), its extracted nonstationarity degree of features is tracked to extract the discriminative features of Gabor atoms. Two experimental examples show that the Gabor filter approach with simple structure has higher recognition rate in radar target recognition from HRRP as compared with several existing methods.     

红外极小目标检测算法研究

低信噪比检测技术是实现红外自动目标识别的基本前提,其性能指标将直接决定系统探测距离的远近,是反映红外低可观测目标识别能力至关重要的一项核心技术。自适应背景估计方法是实现这一目标的有效途径。本文在重点论述几种背景估计常用技术的基础上,提出了红外极小目标的形态滤波优化改进算法,通过理论分析和实验检测表明：该算法简化了形态变...     

一种提高SAR目标识别率的有效方法

在合成孔径雷达自动目标识别SAR ATR中，SAR像的预处理是提高识别率的关键技术之一。给出了一种简单有效的SAR图像预处理方法，该方法首先对SAR目标像进行对数变换后，再做傅立叶变换。经预处理后的SAR像用支持矢量机SVM分类器进行目标识别。实验结果表明：本方法不但有效地提高了目标识别率，而且保证了目标的平移不变性并具有良好的推广能力。     

Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations     

Effects of polarization and resolution on SAR ATR

Lincoln Laboratory is investigating the detection and classification of stationary ground targets using high resolution, fully polarimetric, synthetic aperture radar (SAR) imagery. A study is summarized in which data collected by the Lincoln Laboratory 33 GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR) performance for several polarization/resolution combinations. The Lincoln Laboratory baseline ATR algorithm suite was used, and was optimized for each polarization/resolution case. Both the HH polarization alone and the optimal combination of HH, HV, and VV were evaluated; the resolutions evaluated were 1 ft/spl times/1 ft and 1 m/spl times/1 m. The data set used for this study contained approximately 74 km/sup 2/ of clutter (56 km/sup 2/ of mixed clutter plus 18 km/sup 2/ of highly cultural clutter) and 136 tactical target images (divided equally between tanks and howitzers).