Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture
analysis, or linear unmixing, aims at estimating the number of reference substances, also …

摘要受高光谱成像仪低空间分辨率及复杂地物的影响, 高光谱遥感图像存在大量混合像元.
为提高地表分类精度以及满足亚像元级目标探测的需求, 混合像元分解技术一直是高光谱遥感 …

Hyperspectral image classification has been an active topic of research in recent years. In
the past, many different types of features have been extracted (using both linear and …

Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral
images, which decomposes a mixed pixel into a collection of constituent materials weighted …

Endmember extraction is a process to identify the hidden pure source signals from the
mixture. In the past decade, numerous algorithms have been proposed to perform this …

With the maturation of satellite technology, Hyperspectral Remote Sensing (HRS) platforms
have developed from the initial ground-based and airborne platforms into spaceborne …

In hyperspectral image analysis, the principal components analysis (PCA) and the maximum
noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR) …

This paper studies a fully Bayesian algorithm for endmember extraction and abundance
estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed …

Independent component analysis (ICA) has recently been proposed as a tool to unmix
hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a …