This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

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

Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with
high spatial and temporal resolutions using multisource images. Using spectral unmixing …

With advantages such as non-destructiveness, high sensitivity and high accuracy, optical
techniques have successfully integrated into various important physical quantities in …

Spatiotemporal fusion in remote sensing plays an important role in Earth science
applications by using information complementarity between different remote sensing data to …

Advances in hyperspectral sensors have led to a significantly increased capability for high-
quality data. This trend calls for the development of new techniques to enhance the way that …

Hyperspectral data compression and dimensionality reduction has received considerable
interest in recent years due to the high spectral resolution of these images. Contrarily to the …

Compressive sensing promises to enable bandwidth-efficient on-board compression of
astronomical data by lifting the encoding complexity from the source to the receiver. The …

Recently, compressive sensing (CS) has offered a new framework whereby a signal can be
recovered from a small number of noisy non-adaptive samples. This is now an active area of …

The hyperspectral snapshot compressive imaging (SCI) system encodes three-dimensional
hyperspectral images into a single two-dimensional snapshot measurement and then …