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摘要翻译：
本文研究了在甚高分辨率合成孔径雷达(SAR)图像中自动检测人造结构尤其是建筑物的难题。在此背景下，本文有两个主要贡献：首先，提出了一种新的通用工作流程，该流程首先利用先进的干涉技术（即SAR层析成像技术）对VHR SAR图像栈进行处理，并借助辅助信息（即使用公开的二维建筑足迹或采用光学图像分类方案）将星载TomoSAR点云划分为建筑物和非建筑物，然后将提取的建筑物点反投影到SAR成像坐标上，生成自动的大规模基准标记（建筑物/非建筑物）SAR数据集。其次，利用这些标记数据集（即建筑物掩码）构造并训练了具有附加条件随机场的递归神经网络的最先进的深度全卷积神经网络，以检测单个VHR SAR图像中的建筑物区域。这种层叠结构已经成功地应用于计算机视觉和遥感领域的光学图像分类，但据我们所知，尚未应用于SAR图像。在TerraSAR-X VHR聚束SAR图像上对建筑物检测的结果进行了验证，该图像覆盖范围约为39km^2$$-$,几乎覆盖整个柏林城市，平均像素精度约为93.84%
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英文标题：
《Buildings Detection in VHR SAR Images Using Fully Convolution Neural
Networks》
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作者：
Muhammad Shahzad, Michael Maurer, Friedrich Fraundorfer, 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：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：
This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images. In this context, the paper has two major contributions: Firstly, it presents a novel and generic workflow that initially classifies the spaceborne TomoSAR point clouds $ - $ generated by processing VHR SAR image stacks using advanced interferometric techniques known as SAR tomography (TomoSAR) $ - $ into buildings and non-buildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR datasets. Secondly, these labelled datasets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km$ ^2 $ $ - $ almost the whole city of Berlin $ - $ with mean pixel accuracies of around 93.84%
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PDF链接：
https://arxiv.org/pdf/1808.06155
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