A Multilinear Mixing Model for Nonlinear Spectral Unmixing

Multilinear Spectral Decomposition for Nonlinear Maxwell Equations

We study the frequency form, popular in physics, of Maxwell equations for nonlinear spatially inhomogeneous dielectric media. Using wavepackets based on eigenmodes of the underlying linear dielectric medium we develop a mathematically consistent interpretation of the frequency form of nonlinear Maxwell operators. In particular, we construct the operator of nonlinear polarization based on freque...

An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data

The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...

Nonlinear Semi-Supervised Hyperspectral Unmixing Via Semantic Spectral Representation

This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library. Existing semisupervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most such methods assume a linear mixing model. Howeve...

Spectral Unmixing for Information Extraction

“From pixels to processes” – this the shortest and most essential formulation of all studies, experiments and investigations carried out in the field of Earth remote sensing observations. This formulation reveals two main directions of data interpretation: the first related to classification and feature retrieval, and the second associated with multi-temporal aspects of remotely sensed data and...

Associative morphological memories for spectral unmixing