Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …

In this article, we propose a minimum simplex convolutional network (MiSiCNet) for deep
hyperspectral unmixing. Unlike all the deep learning-based unmixing methods proposed in …

Deep learning (DL) has heavily impacted the data-intensive field of remote sensing.
Autoencoders are a type of DL methods that have been found to be powerful for blind …

Spectral unmixing is central when analyzing hyperspectral data. To accomplish this task,
physics-based methods have become popular because, with their explicit mixing models …

Hyperspectral unmixing is recognized as an important tool to learn the constituent materials
and corresponding distribution in a scene. The physical spectral mixture model is always …

The deep image prior showed that a randomly initialized network with a suitable architecture
can be trained to solve inverse imaging problems by simply optimizing it's parameters to …

Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral
image (HSI) applications. Over the past few years, the linear mixing model (LMM) has …

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

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 …

Deep neural networks have shown huge potential in hyperspectral unmixing (HU). However,
the large function space increases the difficulty of obtaining the optimal solution with limited …