A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …

Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures
(also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral …

Hyperspectral unmixing is a crucial preprocessing step for material classification and
recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions …

Convex optimization problems are common in hyperspectral unmixing. Examples are the
constrained least squares (CLS) problem used to compute the fractional abundances in a …

Hyperspectral image destriping is a challenging and promising theme in remote sensing.
Striping noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely …

Sparse unmixing, as a semisupervised unmixing method, has attracted extensive attention.
The process of sparse unmixing involves treating the mixed pixels of hyperspectral imagery …

高光谱遥感影像数据具有高维特征, 信息冗余, 不确定性显著, 小样本, 空谱合一等特征,
对其进行数据处理面临巨大挑战, 高光谱遥感影像降维是高光谱遥感的重要研究方向之一 …

Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to
estimating the abundance of pure spectral constituents (endmembers) in each (possibly …

Endmember extraction is the process of selecting a collection of pure signature spectra of
the materials present in a remotely sensed hyperspectral scene. These pure signatures are …