Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution
(SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral …

Hyperspectral videos (HSVs) play an important role in the monitoring domain, as they can
provide more information than red–green–blue (RGB) videos about the movement of …

Spectral super-resolution is a very important technique to obtain hyperspectral images from
only multispectral images, which can effectively solve the high acquisition cost and low …

Recently, the compressed sensing (CS) theory has become an interesting topic for pan-
sharpening of multispectral images. The CS theory ensures that, under the sparsity …

Despite the importance of sparsity signal models and the increasing prevalence of high-
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …

High-spectral resolution imaging systems play a critical role in the identification and
characterization of objects in a scene of interest. Unfortunately, multiple factors impair …

Optical imaging of calcium signals in the brain has enabled researchers to observe the
activity of hundreds-to-thousands of individual neurons simultaneously. Current methods …

Acquisition of multi-and hyperspectral imagery imposes significant requirements on the
hardware capabilities of the sensors involved. In order to keep costs manageable, and due …

In this paper, a new method is presented for spatial resolution enhancement of
hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation …

This paper presents a new spectral-spatial feature learning method for hyperspectral image
classification, which integrates spectral and spatial information into group sparse coding …