摘要翻译：
从场景中的其他自然和人为杂波目标中分类隐藏和遮蔽感兴趣的目标是美国陆军面临的一个重要问题。目标通常由低频超宽带合成孔径雷达(SAR)捕获的信号来表示。这项技术已被用于各种应用，包括地面穿透和通过墙壁的传感。然而，该技术仍然面临着一个重要的问题，即在该特定频段的SAR图像分辨率低，雷达散射截面低，目标相对于雷达信号波长小，干扰大。基于稀疏表示的分类(SRC)方法首先和部分地解决了分类问题，该方法能够从信号中提取噪声并利用跨信道信息。尽管提供了潜在的结果，但SRC相关方法在表示非线性关系和处理较大的训练集方面存在缺陷。本文提出了一种同时分解分类网络(SDCN)来减少噪声推断，提高分类精度。该网络包含两个联合训练的子网络：分解子网络处理去噪，分类子网络区分目标和混淆者。实验结果表明，在没有分解和SRC相关方法的网络上有显著的改进。
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英文标题：
《Deep Network for Simultaneous Decomposition and Classification in
  UWB-SAR Imagery》
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作者：
Tiep Vu, Lam Nguyen, Tiantong Guo, Vishal Monga
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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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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issues regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods.
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PDF链接：
https://arxiv.org/pdf/1801.05458