scholarly journals Spectral Unmixing: A Derivation of the Extended Linear Mixing Model From the Hapke Model

2020 ◽  
Vol 17 (11) ◽  
pp. 1866-1870 ◽  
Author(s):  
Lucas Drumetz ◽  
Jocelyn Chanussot ◽  
Christian Jutten
2021 ◽  
pp. 108214
Author(s):  
Saeideh Ghanbari Azar ◽  
Saeed Meshgini ◽  
Soosan Beheshti ◽  
Tohid Yousefi Rezaii

2021 ◽  
pp. 1-27
Author(s):  
Fernando Luis Hillebrand ◽  
Ulisses Franz Bremer ◽  
Marcos Wellausen Dias de Freitas ◽  
Juliana Costi ◽  
Cláudio Wilson Mendes Júnior ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haonan Zhang ◽  
Xingping Wen ◽  
Junlong Xu ◽  
Dayou Luo ◽  
Ping He

In the spectrum measurement experiment, the roughness of the object surface is an essential factor that cannot be ignored. In this experiment, a group of mixed pixel samples with different mixing ratios were designed, and these samples were printed on four kinds of papers with different roughness. The spectral characteristics of mixed pixels with different roughness are quantitatively analyzed by using the measured spectral data. The linear spectral mixture model is used for spectral decomposition, and the effect of roughness on the unmixing precision of mixed pixels was studied. The surface roughness will affect the reflectivity of the mixed pixel. Specifically, the higher the roughness is, the higher the reflectivity of the sample is. This phenomenon is more noticeable when the proportion of white endmember (PWE) is large, and as the white area ratio decreases, the reflectance difference gradually decreases. When the surface roughness of the sample is less than 3.339 μm, the spectral decomposition is performed using a linear spectral mixing model in the visible light band. The average error of the unmixing is less than 0.53%, which is lower than the conventional standard spectral measurement error. In other words, when the surface roughness of the sample is controlled within a specific range, the effect of roughness on the unmixing accuracy of the mixed pixels is small, and this effect can be almost ignored. Multiple scattering within the pixels is the key to model selection and unmixing accuracy, when using the ASD FieldSpec3 spectrometer to perform spectral reflectance measurement and linear spectral unmixing experiments. If the surface roughness of the sample to be measured is less than the maximum wavelength of the spectrometer, the experimental results believe that the photon energy is mainly mirror reflection on the surface of the object and diffuse reflection. At this time, it is still a better choice to use a linear spectral mixing model to decompose the mixed pixels.


2008 ◽  
Author(s):  
David Gillis ◽  
Jeffrey Bowles ◽  
Emmett J. Ientilucci ◽  
David W. Messinger

2019 ◽  
Vol 11 (19) ◽  
pp. 2188
Author(s):  
Li ◽  
Zhu ◽  
Guo ◽  
Chen

Spectral unmixing of hyperspectral images is an important issue in the fields of remotesensing. Jointly exploring the spectral and spatial information embedded in the data is helpful toenhance the consistency between mixing/unmixing models and real scenarios. This paper proposesa graph regularized nonlinear unmixing method based on the recent multilinear mixing model(MLM). The MLM takes account of all orders of interactions between endmembers, and indicates thepixel-wise nonlinearity with a single probability parameter. By incorporating the Laplacian graphregularizers, the proposed method exploits the underlying manifold structure of the pixels’ spectra,in order to augment the estimations of both abundances and nonlinear probability parameters.Besides the spectrum-based regularizations, the sparsity of abundances is also incorporated for theproposed model. The resulting optimization problem is addressed by using the alternating directionmethod of multipliers (ADMM), yielding the so-called graph regularized MLM (G-MLM) algorithm.To implement the proposed method on large hypersepectral images in real world, we proposeto utilize a superpixel construction approach before unmixing, and then apply G-MLM on eachsuperpixel. The proposed methods achieve superior unmixing performances to state-of-the-artstrategies in terms of both abundances and probability parameters, on both synthetic and real datasets.


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