This paper establishes a review of the recent study in the field of hyperspectral (HS) image
compression approaches. Recently, image compression techniques have achieved …

Sparse representations over redundant dictionaries have shown to produce high-quality
results in various signal and image processing tasks. Recent advancements in learning …

The shape, size and distribution of the microstructure could certainly reveal mechanical
properties. Therefore, it is important to segment the microstructure accurately. However, in …

A sparse and collaborative representation-based detector (SCRD) is proposed in this work.
It uses the benefits of both sparse and collaborative representation for anomalous target …

In the compressed processing of hyperspectral images, orthogonal matching pursuit
algorithm (OMP) can be used to obtain sparse decomposition results. Aimed at the time …

In this study, proximity based optimization algorithms are used for lossy compression of
hyperspectral images that are inherently large scale. This is the first time that such proximity …

A new Bayesian-based method is developed for unmixing of hyperspectral images.
Endmembers are assumed variable based on the Gaussian distribution. A semi-supervised …

This article investigates a novel visualization-based fusion of hyperspectral image bands
using an iterative approach. Given a multi-objective function and the pixel-based …

Multispectral image encoding/decoding methods using spectral dictionary learning and
sparse representation to fully exploit spectral features are proposed. In the scheme, K-SVD …

A typical visible light spectrum for a human is about 400 nanometres (nm) to 700 nm in
wavelength. Hyperspectral (HS) images include hundreds of narrow and contiguous …