摘要翻译：
无监督变化检测技术通常局限于通过具有相同空间分辨率和光谱分辨率的传感器在不同时间获取的两幅多波段光学图像。这种情况适合于同源像素的直接比较，例如像素差分。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类型的不同分辨率的传感器获得的图像。近年来，人们提出了一些针对不同空间分辨率和光谱分辨率图像的变化检测技术。然而，它们集中在一个特定的场景中，其中一个图像具有高空间分辨率和低光谱分辨率，而另一个图像具有低空间分辨率和高光谱分辨率。本文讨论了在不考虑空间分辨率和光谱分辨率差异的情况下检测任意两幅多波段光学图像之间变化的问题。我们提出了一种有效利用现有信息的方法，将两幅观测图像建模为具有相同高空间分辨率和高光谱分辨率的两幅（未观测）潜在图像的空间和光谱退化版本。在覆盖同一场景的情况下，除了空间稀疏位置可能发生变化外，潜在图像应该是全局相似的。因此，通过强健的融合任务来设想变化检测任务，该融合任务强制估计的潜在图像之间的差异在空间上是稀疏的。我们证明了这种鲁棒融合可以被表述为一个反问题，并使用交替最小化策略迭代求解。提出的框架实现了一系列详尽的应用场景，并应用于实际的多波段光学图像。通过与现有的变化检测方法的比较，证明了所提出的鲁棒融合检测策略的准确性。
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英文标题：
《Robust fusion algorithms for unsupervised change detection between
  multi-band optical images - A comprehensive case study》
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作者：
Vinicius Ferraris, Nicolas Dobigeon, Marie Chabert
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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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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：
  Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution. This scenario is suitable for a straight comparison of homologous pixels such as pixel-wise differencing. However, in some specific cases such as emergency situations, the only available images may be those acquired through different kinds of sensors with different resolutions. Recently some change detection techniques dealing with images with different spatial and spectral resolutions, have been proposed. Nevertheless, they are focused on a specific scenario where one image has a high spatial and low spectral resolution while the other has a low spatial and high spectral resolution. This paper addresses the problem of detecting changes between any two multi-band optical images disregarding their spatial and spectral resolution disparities. We propose a method that effectively uses the available information by modeling the two observed images as spatially and spectrally degraded versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. Covering the same scene, the latent images are expected to be globally similar except for possible changes in spatially sparse locations. Thus, the change detection task is envisioned through a robust fusion task which enforces the differences between the estimated latent images to be spatially sparse. We show that this robust fusion can be formulated as an inverse problem which is iteratively solved using an alternate minimization strategy. The proposed framework is implemented for an exhaustive list of applicative scenarios and applied to real multi-band optical images. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed robust fusion-based strategy.
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PDF链接：
https://arxiv.org/pdf/1804.03068