Linear Unmixing

2021 ◽  
pp. 1-3
Author(s):  
Katherine L. Silversides
Keyword(s):  
2021 ◽  
Author(s):  
◽  
J. N. Mendoza Chavarría

Spectral unmixing has proven to be a great tool for the analysis of hyperspectral data, with linear mixing models (LMMs) being the most used in the literature. Nevertheless, due to the limitations of the LMMs to accurately describe the multiple light scattering effects in multi and hyperspectral imaging, new mixing models have emerged to describe nonlinear interactions. In this paper, we propose a new nonlinear unmixing algorithm based on a multilinear mixture model called Non-linear Extended Blind Endmember and Abundance Extraction (NEBEAE), which is based on a linear unmixing method established in the literature. The results of this study show that proposed method decreases the estimation errors of the spectral signatures and abundance maps, as well as the execution time with respect the state of the art methods.


Author(s):  
Xiaojing Liu ◽  
Lingmei Jiang ◽  
Gongxue Wang ◽  
Shirui Hao ◽  
Zhizhong Chen

Author(s):  
Yongming Liu ◽  
Ruru Deng ◽  
Jun Li ◽  
Yan Qin ◽  
Longhai Xiong ◽  
...  
Keyword(s):  

2019 ◽  
Vol 27 (13) ◽  
pp. 18282
Author(s):  
Chenshuang Zhang ◽  
Yangpei Liu ◽  
Wenfeng Qu ◽  
Wenhua Su ◽  
Mengyan Du ◽  
...  

2022 ◽  
Vol 10 (1) ◽  
Author(s):  
Roxanne Radpour ◽  
Glenn A. Gates ◽  
Ioanna Kakoulli ◽  
John K. Delaney

AbstractImaging spectroscopy (IS) is an important tool in the comprehensive technical analysis required of archaeological paintings. The complexity of pigment mixtures, diverse artistic practices and painting technologies, and the often-fragile and weathered nature of these objects render macroscale, non-invasive chemical mapping an essential component of the analytical protocol. Furthermore, the use of pigments such as Egyptian blue and madder lake, featuring diagnostic photoluminescence emission, provides motivation to perform photoluminescence mapping on the macroscale. This work demonstrates and advances new applications of dual-mode imaging spectroscopy and data analysis approaches for ancient painting. Both reflectance (RIS) and luminescence (LIS) modes were utilized for the study of a Roman Egyptian funerary portrait from second century CE Egypt. The first derivative of the RIS image cube was analyzed and found to significantly improve materials separation, identification, and the extent of mapping. Egyptian blue and madder lake were mapped across a decorated surface using their luminescence spectral signatures in the region of 540–1000 nm as endmembers in LIS analyses. Linear unmixing of the LIS endmembers and subsequent derivative analyses resulted in an improved separation and mapping of the luminescence pigments. RIS and LIS studies, combined with complementary, single-spot collection elemental and molecular spectroscopy, were able to successfully characterize the portrait’s painting materials and binding media used by the ancient artist, providing key insight into their material use, stylistic practices, and technological choices.


Icarus ◽  
2014 ◽  
Vol 237 ◽  
pp. 61-74 ◽  
Author(s):  
Frédéric Schmidt ◽  
Maxime Legendre ◽  
Stéphane Le Mouëlic

2021 ◽  
Vol 87 (6) ◽  
pp. 431-443
Author(s):  
Hui Luo ◽  
Nan Chen

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.


Author(s):  
R. Kannan ◽  
A. V. Ievlev ◽  
N. Laanait ◽  
M. A. Ziatdinov ◽  
R. K. Vasudevan ◽  
...  

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