单目数字高程模型(DEM)的估计 卷积神经网络在合成孔径雷达中的应用 图像-经管之家官网！

摘要翻译：
合成孔径雷达(SAR)干涉测量（InSAR）是利用重复通过几何学进行的。利用InSAR技术估算地表地形重建。距离-多普勒聚焦技术的主要问题是二维SAR结果的性质，它受中途停留不确定的影响。为了解决这个问题，至少需要两个传感器采集，由基线隔开并在交叉倾斜范围内扩展。然而，由于其多时相特性，这些技术除了受到物理平台不稳定性的影响外，还容易受到大气和地球环境参数变化的影响。此外，要么需要两个雷达，要么需要一个干涉周期（从几天到几周），这使得实时DEM估计不可能。在这项工作中，作者提出了一种新的实验替代InSAR方法，使用一种由深度神经网络实现的数据驱动方法。我们提出了一种完全卷积神经网络(CNN)编解码器结构，并在雷达图像上训练它，以便从单次获取的图像中估计DEMs。我们在一组哨兵图像上的结果表明，该方法能够在一定程度上学习DEM的统计特性。这种探索性分析的结果令人鼓舞，并为用数据驱动方法解决单次DEM估计问题开辟了道路。
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英文标题：
《Towards Monocular Digital Elevation Model (DEM) Estimation by
Convolutional Neural Networks - Application on Synthetic Aperture Radar
Images》
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作者：
Gabriele Costante and Thomas A. Ciarfuglia and Filippo Biondi
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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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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：
Synthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surface. The main problem of the range-Doppler focusing technique is the nature of the two-dimensional SAR result, affected by the layover indetermination. In order to resolve this problem, a minimum of two sensor acquisitions, separated by a baseline and extended in the cross-slant-range, are needed. However, given its multi-temporal nature, these techniques are vulnerable to atmosphere and Earth environment parameters variation in addition to physical platform instabilities. Furthermore, either two radars are needed or an interferometric cycle is required (that spans from days to weeks), which makes real time DEM estimation impossible. In this work, the authors propose a novel experimental alternative to the InSAR method that uses single-pass acquisitions, using a data driven approach implemented by Deep Neural Networks. We propose a fully Convolutional Neural Network (CNN) Encoder-Decoder architecture, training it on radar images in order to estimate DEMs from single pass image acquisitions. Our results on a set of Sentinel images show that this method is able to learn to some extent the statistical properties of the DEM. The results of this exploratory analysis are encouraging and open the way to the solution of single-pass DEM estimation problem with data driven approaches.
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PDF链接：
https://arxiv.org/pdf/1803.05387