scholarly journals Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery

2012 ◽  
Vol 21 (6) ◽  
pp. 3017-3025 ◽  
Author(s):  
Yoann Altmann ◽  
Abderrahim Halimi ◽  
Nicolas Dobigeon ◽  
Jean-Yves Tourneret
Keyword(s):  
Hyperspectral Imagery ◽  
Mixing Model ◽  
Spectral Unmixing

scholarly journals Study on the Effect of Surface Roughness on the Spectral Unmixing of Mixed Pixels

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haonan Zhang ◽  
Xingping Wen ◽  
Junlong Xu ◽  
Dayou Luo ◽  
Ping He
Keyword(s):  
Surface Roughness ◽  
Spectral Decomposition ◽  
Spectral Characteristics ◽  
Mixing Model ◽  
Spectral Unmixing ◽  
Mirror Reflection ◽  
Reflectance Measurement ◽  
Mixed Pixel ◽  
Mixed Pixels ◽  
Spectral Mixing

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.

A generalized linear mixing model for hyperspectral imagery

2008 ◽  
Author(s):  
David Gillis ◽  
Jeffrey Bowles ◽  
Emmett J. Ientilucci ◽  
David W. Messinger
Keyword(s):  
Hyperspectral Imagery ◽  
Mixing Model ◽  
Linear Mixing Model

scholarly journals A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing

2019 ◽  
Vol 11 (19) ◽  
pp. 2188
Author(s):  
Li ◽  
Zhu ◽  
Guo ◽  
Chen
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Spatial Information ◽  
Mixing Model ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Manifold Structure ◽  
Underlying Manifold

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.

scholarly journals Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2305 ◽  
Author(s):  
Zhongliang Wang ◽  
Hua Xiao
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Interpolation Method ◽  
Side Information ◽  
Hyperspectral Imagery ◽  
Original Data ◽  
Distributed Source Coding ◽  
Spectral Unmixing ◽  
Compressed Video ◽  
Distributed Compressed Video Sensing

The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.

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