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 …

A novel method for anomaly detection in hyperspectral images is proposed. The method is
based on two ideas. First, compared with the surrounding background, objects with …

Hyperspectral sensors are passive instruments that record reflected electromagnetic
radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two …

Anomaly detection has received considerable interest for hyperspectral data exploitation
due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly …

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures
are different from their background. Anomalies are often man-made targets. With such target …

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

To enhance spectral unmixing performance, a large number of algorithms have
simultaneously investigated spatial and spectral information in hyperspectral images …

Spatial-spectral classification is a very important topic in the field of remotely sensed
hyperspectral imaging. In this work, we develop a parallel implementation of a novel …

We present a high-performance GPU kernel with a substantial speedup over vendor
libraries for very small matrix computations. In addition, we discuss most of the challenges …

In hyperspectral anomaly detection, the dual-window-based detector is a widely used
technique that employs two windows to capture nonstationary statistics of anomalies and …