Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation.
The spectral signatures collected in natural environments are invariably a mixture of the …

This paper develops a new approach to band subset selection (BSS) for hyperspectral
image classification (HSIC) which selects multiple bands simultaneously as a band subset …

In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental
problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure …

Nonnegative matrix factorization (NMF) has been widely used in hyperspectral unmixing
(HU). However, most NMF-based methods have single-layer structures, which may achieve …

Anomaly endmembers play an important role in the application of remote sensing, such as
in unmixing classification and target detection. Inspired by the iterative error analysis (IEA), a …

Due to instrument instability, slit contamination, and light interference, hyperspectral images
often suffer from striping artifacts, which greatly impairs the data quality. Real hyperspectral …

In this paper we propose an ι 1-norm penalized sparse support vector machine (SSVM) as
an embedded approach to the hyperspectral imagery band selection problem. SSVMs …

Spectral unmixing is an important problem in hyperspectral data exploitation. It amounts at
characterizing the mixed spectral signatures collected by an imaging instrument in the form …

This paper presents an approach to band selection fusion (BSF) which fuses bands
produced by a set of different band selection (BS) methods for a given number of bands to …

This paper develops an information theoretical approach using channel capacity as a
criterion for band subset selection (BSS). It formulates a BSS problem as a channel capacity …