Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into
a set of endmembers and their corresponding abundances. Endmembers are spectra of the …

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

Leaves interact with light in ways that create a spectral footprint of the terrestrial environment
of our planet. Most of the visible light penetrating the Earth's atmosphere is absorbed by …

Nonlinear models have recently shown interesting properties for spectral unmixing. This
paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for …

This paper presents a new approach for hyperspectral image classification exploiting
spectral-spatial information. Under the maximum a posteriori framework, we propose a …

In hyperspectral unmixing, bilinear and linear-quadratic models have become popular
recently, and also the polynomial postnonlinear model shows promising results. These …

Spectral unmixing is a major technique for the further development of hyperspectral
analysis. It aims to determine the corresponding proportion (fractional abundance) of the …

Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and
satellite (or high-resolution, HR) images, has been applied to many application domains …

Linear spectral unmixing is an essential technique in hyperspectral image (HSI) processing
and interpretation. In recent years, deep learning-based approaches have shown great …