Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

2007 ◽  
Author(s):  
Yue Yu ◽  
Shan Guo ◽  
Weidong Sun
Keyword(s):  
Matrix Factorization ◽  
Minimum Distance ◽  
Hyperspectral Images ◽  
Endmember Extraction ◽  
Non Negative Matrix Factorization

scholarly journals Using a Panchromatic Image to Improve Hyperspectral Unmixing

2020 ◽  
Vol 12 (17) ◽  
pp. 2834
Author(s):  
Simon Rebeyrol ◽  
Yannick Deville ◽  
Véronique Achard ◽  
Xavier Briottet ◽  
Stephane May
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Endmember Extraction ◽  
Local Approach ◽  
Hyperspectral Unmixing ◽  
Panchromatic Image ◽  
Art Methods ◽  
Non Negative Matrix Factorization

Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers.

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 THE COMPARISON BETWEEN NMF AND ICA IN PIGMENT MIXTURE IDENTIFICATION OF ANCIENT CHINESE PAINTINGS

2018 ◽  
Vol XLII-3 ◽  
pp. 1169-1172
Author(s):  
Y. Liu ◽  
S. Lyu ◽  
M. Hou ◽  
Q. Yin
Keyword(s):  
Independent Component Analysis ◽  
Spectral Data ◽  
Blind Source Separation ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Endmember Extraction ◽  
Simulation Data ◽  
Mixed Spectrum ◽  
Pigment Type ◽  
Non Negative Matrix Factorization

Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

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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