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[电气工程与系统科学] 高光谱图像的部分异步分布式解混 [推广有奖]

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能者818 在职认证  发表于 2022-3-4 21:01:30 来自手机 |只看作者 |坛友微信交流群|倒序 |AI写论文

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摘要翻译:
到目前为止,在遥感文献中,只有少数几种专门的策略专门解决了大规模或多时相高光谱数据集的混合问题。其中,在分布式估计框架内进行了一些尝试,特别是依赖于乘数交替方向法(ADMM)。在本文中,我们提出在最近提出的异步算法的基础上研究一个部分异步分布式解混过程的兴趣。在标准的假设下,该算法继承了非凸优化的最新贡献,同时允许有效地解决感兴趣的问题。与所提出的分解过程的分布式同步对应的比较允许对合成和真实数据的兴趣进行评估。此外,由于它的通用性和灵活性,本文所研究的程序可以用来解决各种矩阵分解问题。
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英文标题:
《Partially Asynchronous Distributed Unmixing of Hyperspectral Images》
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作者:
Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
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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:Mathematics        数学
二级分类:Optimization and Control        优化与控制
分类描述:Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学,线性规划,控制论,系统论,最优控制,博弈论
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一级分类:Physics        物理学
二级分类:Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述:Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件:数据处理与存储;测量方法;统计和数学方面,如参数化和不确定性。
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英文摘要:
  So far, the problem of unmixing large or multitemporal hyperspectral datasets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular relying on the alternating direction method of multipliers (ADMM). In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in non-convex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this work can be implemented to address various matrix factorization problems.
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PDF链接:
https://arxiv.org/pdf/1710.02574
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关键词:分布式 Optimization Mathematical Segmentation Architecture 研究 提出 部分 proposed 兴趣

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