摘要翻译：
由于甚高分辨率SAR图像和光学图像具有不同的成像几何特征，在城市密集区进行联合解译是一项艰巨的任务。特别是由于SAR成像几何形状的侧视而不可避免地造成的中途停留，使得这一任务更具挑战性。直到最近，“Sarptical”框架[1]，[2]才提出了解决这一问题的有希望的解决方案。SARptical通过严格的三维重建和匹配，可以在相应的高分辨率光学图像中跟踪单个SAR散射体。本文介绍了从TerraSAR-X高分辨率聚光图像和航空超分辨率光学图像中提取的光学图像斑块的SARptical数据集，该数据集是由1万多对相应的SAR数据组成的，是由TerraSAR-X高分辨率聚光图像和航空超分辨率光学图像提取的光学图像斑块组成的。该数据集为多传感器数据分析提供了新的机会。人们可以在SAR和光学图像域分析成像对象的几何、材料和其他性质。更高级的应用，如SAR和通过深度学习的光学图像匹配[3]现在也是可能的。
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英文标题：
《The SARptical Dataset for Joint Analysis of SAR and Optical Image in
  Dense Urban Area》
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作者：
Yuanyuan Wang, Xiao Xiang Zhu
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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：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The joint interpretation of very high resolution SAR and optical images in dense urban area are not trivial due to the distinct imaging geometry of the two types of images. Especially, the inevitable layover caused by the side-looking SAR imaging geometry renders this task even more challenging. Only until recently, the "SARptical" framework [1], [2] proposed a promising solution to tackle this. SARptical can trace individual SAR scatterers in corresponding high-resolution optical images, via rigorous 3-D reconstruction and matching. This paper introduces the SARptical dataset, which is a dataset of over 10,000 pairs of corresponding SAR, and optical image patches extracted from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical images. This dataset opens new opportunities of multisensory data analysis. One can analyze the geometry, material, and other properties of the imaged object in both SAR and optical image domain. More advanced applications such as SAR and optical image matching via deep learning [3] is now also possible.
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PDF链接：
https://arxiv.org/pdf/1801.07532