摘要翻译：
变化检测的原型场景通常考虑通过相同模态的传感器获取的两个图像。然而，在某些特定情况下，例如紧急情况下，唯一可用的图像可能是通过不同模式的传感器获得的图像。本文讨论了由不同分辨率的传感器获取的两个观测图像之间的变化的无监督检测问题。这些传感器的差异在操作变化检测的上下文中引入了其他问题，而这些问题是大多数经典方法所没有解决的。本文介绍了一种新的框架，通过将两个观察图像建模为一个稀疏的线性原子组合，这些原子属于从每个观察图像中学习的一对耦合的过完备字典，从而有效地利用可用的信息。由于它们涵盖相同的地理位置，除了稀疏的空间位置可能发生变化之外，代码预计将在全球范围内相似。因此，通过双码估计来设想变化检测任务，该双码估计在与每个图像相关联的估计码之间的差中强制空间稀疏性。将该问题转化为一个反问题，利用一种有效的近似交替极小化算法迭代求解，该算法考虑了非光滑和非凸函数。将该方法应用于真实图像中，具有模拟的真实感和真实的变化。通过与现有变化检测方法的比较，验证了该策略的正确性。
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英文标题：
《Coupled dictionary learning for unsupervised change detection between
  multi-sensor remote sensing images》
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作者：
Vinicius Ferraris, Nicolas Dobigeon, Yanna Cavalcanti, Thomas Oberlin,
  Marie Chabert
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最新提交年份：
2019
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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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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：
  Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors of different modalities. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions. These sensor dissimilarities introduce additional issues in the context of operational change detection that are not addressed by most of the classical methods. This paper introduces a novel framework to effectively exploit the available information by modelling the two observed images as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from each observed image. As they cover the same geographical location, codes are expected to be globally similar, except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes associated with each image. This problem is formulated as an inverse problem which is iteratively solved using an efficient proximal alternating minimization algorithm accounting for nonsmooth and nonconvex functions. The proposed method is applied to real images with simulated yet realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.
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PDF链接：
https://arxiv.org/pdf/1807.08118