Representation learning is an important topic in machine learning, pattern recognition, and
data mining research. Among many representation learning approaches, semi-nonnegative …

It is challenging for current compressive sensing (CS) approaches to reconstruct image from
compressed observations with impulsive noise and outliers, termed robust image CS …

Nonnegative matrix factorization (NMF) can obtain endmembers and abundances
simultaneously, and has attracted a lot of interest in hyperspectral unmixing. However, it is …

Various improved nonnegative matrix factorization (NMF) methods have been widely used
in spectral unmixing (SU), including nonlinear versions to counter for the lower spatial …

This article presents a bi-endmember semi-nonnegative matrix factorization (Semi-NMF)
algorithm based on low-rank and sparse matrix decomposition (LRSMD), referred to as …

Hyperspectral unmixing could provide decomposition for small units in hyperspectral images
(HSIs), allowing accurate analysis of ground objects. Unfortunately, interference such as …

Abstract Linear Mixing Models (LMMs) are the most popular ones used in the linear
hyperspectral unmixing field. However, several of them do not take into account available …

The information society has higher and higher requirements for the collection, transmission
and storage of digital signals, and signal utilization efficiency has become an increasingly …

This article presents a new approach to address the challenge of combining ground-based
LiDAR data with satellite images for cross-view image geo-localization. The task is to figure …

Compressed medical imaging (CMI) is a medical image sampling process with several
samples lower than the Nyquist-Shannon sampling theorem for efficient image sampling; …