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 …

Deep-learning-based methods have attracted increasing attention on hyperspectral
unmixing, where the transformer models have shown promising performance. However …

Extreme operating conditions refer to the critical dynamic state during vehicle operation. The
lack of experimental data under critical conditions is one of the fundamental problems in the …

Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of
the material (endmembers) in each pixel. Most spectral unmixing methods are affected by …

Deep learning (DL) has been proven to be a powerful method in computer vision and is
receiving increasing attention in remote sensing. It is important to analyse the research …

Deep autoencoders have recently been applied to blind hyperspectral unmixing task to
estimate endmembers and their corresponding abundances simultaneously. The objective …

The fusion of multispectral sensor data techniques for sets containing complementary
information about the subject of observation leads to the visualization of data into a form …

Hyperspectral unmixing aims to identify the materials within the pixels of an image and
estimate the corresponding abundance values of these materials. This thesis proposes an …