摘要翻译：
目标检测是合成孔径雷达(SAR)图像自动目标识别系统（SAR-ATR）的前端阶段。探测器的效能直接影响到SAR-ATR处理链的后续阶段。文献中报道了许多实现该探测器的方法。我们提供了一个保护伞，在这个领域的各种研究活动被广泛地探索和分类。首先，对各种检测方法进行了分类。其次，对不同实施策略的基本假设进行了概述。第三，对有代表性的例子进行了列表比较。最后，本文讨论了SAR数据模型的适用性、乘法SAR数据模型的理解以及恒虚警检测的两个独特视角：信号处理和模式识别。从信号处理的角度来看，CFAR是一种有限脉冲响应的带通滤波器。从统计模式识别的角度来看，CFAR是一种次优的单类分类器：欧几里得距离分类器和单参数CFAR和双参数CFAR分别为一个缺失项的二次判别器。我们为SAR图像目标检测的客观设计和实现做出了贡献。
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英文标题：
《Target detection in synthetic aperture radar imagery: a state-of-the-art
  survey》
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作者：
Khalid El-Darymli, Peter McGuire, Desmond Power, Cecilia Moloney
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.
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PDF链接：
https://arxiv.org/pdf/1804.04719