scholarly journals Unsupervised Clustering of Hyperspectral Images of Brain Tissues by Hierarchical Non-negative Matrix Factorization

2016 ◽  
Author(s):  
Bangalore Ravi Kiran ◽  
Bogdan Stanciulescu ◽  
Jesus Angulo
Keyword(s):  
Matrix Factorization ◽  
Unsupervised Clustering ◽  
Hyperspectral Images ◽  
Non Negative Matrix Factorization ◽  
Brain Tissues

scholarly journals HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2045 ◽  
Author(s):  
Haris Khan ◽  
Sofiane Mihoubi ◽  
Benjamin Mathon ◽  
Jean-Baptiste Thomas ◽  
Jon Hardeberg
Keyword(s):  
Spectral Analysis ◽  
High Resolution ◽  
Matrix Factorization ◽  
Local Binary Pattern ◽  
Near Infrared ◽  
Texture Classification ◽  
Hyperspectral Images ◽  
Close Range ◽  
Local Binary Pattern Feature ◽  
Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

scholarly journals Spectral Unmixing of Hyperspectral Images in the Presence of Small Targets

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Sylvain Ravel ◽  
Caroline Fossati ◽  
Salah Bourennane
Keyword(s):  
Matrix Factorization ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Reflectance Spectra ◽  
Hyperspectral Images ◽  
Real World Data ◽  
Linear Combinations ◽  
Resampling Methods ◽  
Small Targets ◽  
Non Negative Matrix Factorization

Generally, the content of the hyperspectral image pixel is a mixture of the reflectance spectra of the different components in the imaged scene. In this paper, we consider a linear mixing model where the pixels are linear combinations of those reflectance spectra, called endmembers, and linear coefficients corresponding to their abundances. An important issue in hyperspectral imagery consists in unmixing those pixels to retrieve the endmembers and their corresponding abundances. We consider the unmixing issue in the presence of small targets, that is, their endmembers are only contained in few pixels of the image. We introduce a thresholding method relying on Non-negative Matrix Factorization to detect pixels containing rare endmembers. We propose two resampling methods based on bootstrap for spectral unmixing of hyperspectral images to retrieve both the dominant and rare endmembers. Our experimental results on both simulated and real world data demonstrate the efficiency of the proposed method to estimate correctly all the endmembers present in hyperspectral images, in particular the rare endmembers.

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