Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

This work proposes a new shadow restoration method for hyperspectral images based on
nonlinear unmixing. A physical model is introduced to estimate the shadowed spectrum from …

Spectral unmixing is a technique for remotely sensed image interpretation that expresses
each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and …

In recent years, deep learning (DL) has attracted increasing attention in hyperspectral
unmixing (HU) applications due to its powerful learning and data fitting ability. The …

In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …

Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most
prominent research topics in signal processing (SP) for hyperspectral remote sensing [1],[2] …

In this article, we introduce a deep learning-based technique for the linear hyperspectral
unmixing problem. The proposed method contains two main steps. First, the endmembers …

In this paper, we present a deep learning based method for blind hyperspectral unmixing in
the form of a neural network autoencoder. We show that the linear mixture model implicitly …

BACKGROUND Imaging technologies, which extend human vision capabilities, are such a
natural part of our current everyday experience that we often take them for granted …