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

Deep learning (DL) has aroused wide attention in hyperspectral unmixing (HU) owing to its
powerful feature representation ability. As a representative of unsupervised DL approaches …

Sparse unmixing, as a semisupervised unmixing method, has attracted extensive attention.
The process of sparse unmixing involves treating the mixed pixels of hyperspectral imagery …

Endmember (EM) spectral variability can greatly impact the performance of standard
hyperspectral image analysis algorithms. Extended parametric models have been …

Recent developments in hyperspectral sensors have made it possible to acquire
hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now …

Image fusion combines data from different heterogeneous sources to obtain more precise
information about an underlying scene. Hyperspectral-multispectral (HS-MS) image fusion is …

Spectral unmixing (SU), which refers to extracting basic features (ie, endmembers) at the
subpixel level and calculating the corresponding proportion (ie, abundances), has become a …

Coupled tensor approximation has recently emerged as a promising approach for the fusion
of hyperspectral and multispectral images, reconciling state of the art performance with …

In this letter, we propose a sparse unmixing technique using a convolutional neural network
(SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based technique …

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of
hyperspectral image sequences. It reveals the dynamical evolution of the materials …