In this work we present a comparative analysis of the performance of two recently proposed algorithms for signal subspace identification (SSI) and dimensionality reduction (DR) in hyperspectral data. Such SSI algorithms are robust to the presence of rare signal components and are particularly suitable when DR is adopted as a pre-processing step in small target detection applications
Hyperspectral data provides rich information and is very useful for a range of applications from gro...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...
This paper deals with the problem of signal subspace estimation and dimensionality reduction (DR) in...
In this paper, we investigate the problem of signal subspace identification (SSI) and dimensionality...
This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in...
Orthogonal subspace projection (OSP) is a powerful tool for dimensionality reduction (DR) in hypersp...
Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis alg...
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms s...
A new technique for signal subspace identification in hyperspectral images is presented. It estimate...
Abstract—Signal subspace identification is a crucial first step in many hyperspectral processing alg...
Hyperspectral imaging sensors provide image data containing both spectral and spatial information fr...
Hyperspectral imaging sensors provide image data containing both spectral and spatial information fr...
Abstract — Hyperspectral imaging sensors provide image data containing both spectral and spatial inf...
Abstract—Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for p...
Hyperspectral data provides rich information and is very useful for a range of applications from gro...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...
This paper deals with the problem of signal subspace estimation and dimensionality reduction (DR) in...
In this paper, we investigate the problem of signal subspace identification (SSI) and dimensionality...
This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in...
Orthogonal subspace projection (OSP) is a powerful tool for dimensionality reduction (DR) in hypersp...
Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis alg...
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms s...
A new technique for signal subspace identification in hyperspectral images is presented. It estimate...
Abstract—Signal subspace identification is a crucial first step in many hyperspectral processing alg...
Hyperspectral imaging sensors provide image data containing both spectral and spatial information fr...
Hyperspectral imaging sensors provide image data containing both spectral and spatial information fr...
Abstract — Hyperspectral imaging sensors provide image data containing both spectral and spatial inf...
Abstract—Linear discriminant analysis (LDA) is a popular approach for dimensionality reduction for p...
Hyperspectral data provides rich information and is very useful for a range of applications from gro...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...
Given an hyperspectral image, the determination of the number of endmembers and the subspace where t...