基于自适应束的光谱变异性高光谱解混 和双重稀疏性

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摘要翻译：
光谱变异性是进行高光谱解混的主要问题之一。在由一些基本材料组成的给定图像中（这里称为端元类），表征这些类的光谱特征可由于固有成分波动或外部因素（照明）而在空间上变化。这些冗余的多端元谱在每类中不利地影响解混方法的性能。本文提出了一种混合模型，它明确地结合了代表每个类的冗余多谱的层次结构。该方法旨在提高每个像素内光谱和类选择的稀疏性。所得到的解混算法能够自适应地恢复与每个类相关的多个端元谱束，并鲁棒地估计丰度。此外，它的灵活性允许在高光谱图像的每个像素内存在可变数量的类，以未混合。将所提出的方法与现有的结合稀疏性的混合方法进行了比较，这些方法同时使用了模拟和真实的高光谱数据。结果表明，该方法可以成功地确定每个类中存在的可变类数，并估计相应的类丰度。
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英文标题：
《Hyperspectral unmixing with spectral variability using adaptive bundles
  and double sparsity》
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作者：
Tatsumi Uezato, Mathieu Fauvel and Nicolas Dobigeon
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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：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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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

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英文摘要：
  Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signature characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances.
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PDF链接：
https://arxiv.org/pdf/1804.11132

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关键词：变异性 Mathematical Architecture Fluctuations Segmentation 因素 方法 能够 光谱 sparsity