Hyperspectral imaging is an indispensable technology for many remote sensing
applications, yet expensive in terms of computing resources. It requires significant …

The classification accuracy of remote sensing data can be increased by integrating ancillary
data provided by multisource acquisition of the same scene. We propose to merge the …

Single-pixel imaging technology is an attractive technology considering the increasing
demand of imagers that can operate in wavelengths where traditional cameras have limited …

Hyperspectral compressive sensing (HCS) can greatly reduce the enormous cost of
hyperspectral images (HSIs) on imaging, storage, and transmission by only collecting a few …

The ability to accurately represent a hyperspectral image (HSI) as a combination of a small
number of elements from an appropriate dictionary underpins much of the recent progress in …

Compressive sensing (CS) has been exploited for hypespectral image (HSI) compression in
recent years. Though it can greatly reduce the costs of computation and storage, the …

Linear unmixing-based compressive sensing has been extensively exploited for
hyperspectral image (HSI) compression in recent years among which gradient sparsity is …

In this paper, a hyperspectral feature extraction method is proposed. A low-rank linear model
using the right eigenvector of the observed data is given for hyperspectral images. A total …

Hyperspectral compressed sensing (HCS) based on spectral unmixing technique has
shown great reconstruction performance. In particular, the linear mixed model (LMM) has …

To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive
measurements, we present a novel manifold-structured sparsity prior based hyperspectral …