Authors:
Abir Zidi
1
;
Julien Marot
2
;
Klaus Spinnler
1
and
Salah Bourennane
2
Affiliations:
1
Fraunhofer IIS, Germany
;
2
Institut Fresnel, France
Keyword(s):
Non-negative Matrix Factorization, PARAFAC, Linear Algebra, Hyperspectral Image, Remote Sensing, Tensor.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Multimodal and Multi-Sensor Models of Image Formation
Abstract:
In the literature, there are several methods for multilinear source separation. We find the most popular ones
such as nonnegative matrix factorization (NMF), canonical polyadic decomposition (PARAFAC). In this paper,
we solved the problem of the hyperspectral imaging with NMF algorithm. We based on the physical property
to improve and to relate the output endmembers spectra to the physical properties of the input data. To
achieve this,we added a regularization which enforces the closeness of the output endmembers to automatically
selected reference spectra. Afterwards we accounted for these reference spectra and their locations in the
initialization matrices. To illustrate our methods, we used self-acquired hyperspectral images (HSIs). The first
scene is compound of leaves at the macroscopic level. In a controlled environment, we extract the spectra of
three pigments. The second scene is acquired from an airplane: We distinguish between vegetation, water, and
soil.