Hyperspectral imaging is an important technique in remote sensing which is characterized
by high spectral resolutions. With the advent of new hyperspectral remote sensing missions …

Remotely sensed hyperspectral imaging is a very active research area, with numerous
contributions in the recent scientific literature. The analysis of these images represents an …

Sub-pixel map** techniques predict the spatial distribution of endmember abundances
which are estimated through spectral unmixing. The sub-pixel map** and spectral …

Linear spectral unmixing is nowadays an essential tool to analyze remotely sensed
hyperspectral images. Although many different contributions have been uncovered during …

Target and anomaly detection are important techniques for remotely sensed hyperspectral
data interpretation. Due to the high dimensionality of hyperspectral data and the large …

Onboard processing systems are becoming very important in remote sensing data
processing. However, a main problem with specialized hardware architectures used for …

Timely detection of targets continues to be a relevant challenge for hyperspectral remote
sensing capability. The automatic target-generation process using an orthogonal projection …

Hyperspectral images taken by satellites pose a challenge for data transmission.
Communication with Earth's antennas is usually time restricted and bandwidth is very …

This letter presents a new parallel method for hyperspectral unmixing composed by the
efficient combination of two popular methods: vertex component analysis (VCA) and sparse …

Spectral unmixing (SU) is a technique to characterize mixed pixels in hyperspectral images
measured by remote sensors. Most of the spectral unmixing algorithms are developed using …