Fast Implementation of Maximum Simplex Volume-Based Endmember Extraction in Original Hyperspectral Data Space

Wang, Liguo; Wei, Fangjie; Liu, Danfeng; Wang, Qunming

doi:10.1109/jstars.2012.2234439

Cited by 21 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This subject is beyond the scope of this paper. Nevertheless, several geometric approaches to SGA have been recently developed in [26]- [31] to address these issues.…”

Section: Discussionmentioning

confidence: 99%

Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Chang

Chen

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. Pixel purity index (PPI) and N-finder algorithm (N-FINDR) are probably the two most widely used techniques for this purpose where many currently available endmember finding algorithms are indeed derived from these two algorithms and can be considered as their variants. Among them are three well-known algorithms derived from imposing different abundance constraints, that is, abundance-unconstrained automatic target generation process (ATGP), abundance nonnegativity constrained vertex component analysis (VCA), and fully abundance constrained simplex growing algorithm (SGA). This paper explores relationships among these three algorithms and further shows that theoretically they are essentially the same algorithms in the sense of design rationale. The reason that these three algorithms perform differently is not because they are different algorithms, but rather because they use different preprocessing steps, such as initial conditions and dimensionality reduction transforms. Index Terms-Automatictarget generation process (ATGP), endmember finding algorithm (EFA), growing simplex algorithm (SGA), linear spectral unmixing (LSU), N-finder algorithm (N-FINDR), orthogonal projection (OP), pixel purity index (PPI), simplex volume (SV), vertex component analysis (VCA).

show abstract

“…This subject is beyond the scope of this paper. Nevertheless, several geometric approaches to SGA have been recently developed in [26]- [31] to address these issues.…”

Section: Discussionmentioning

confidence: 99%

Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Chang

Chen

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Many hyperspectral unmixing algorithms have been proposed over the last several decades, including pixel purity index (PPI), N-FINDR, vertex component analysis (VCA), nonnegative matrix factorization (NMF), and so on [6]- [13]. Among these methods, NMF can simultaneously decompose mixed pixels into endmembers and abundances with physical meaning, without the requirement of existence of pure pixels in hyperspectral data.…”

Section: Geometric Nonnegative Matrix Factorization (Gnmf) For Hypersmentioning

confidence: 99%

Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing

Yang

Zhang

Yao

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In the available hyperspectral unmixing approaches, hyperspectral images are often treated as a list of spectral measurements with no geometric organization. In this paper, we explore the geometric structure of hyperspectral images in both the spatial domain and the spectral domain to advance a geometric nonnegative matrix factorization (GNMF) for more accurate endmember extraction and abundance estimation. In the proposed GNMF, we define the "spatial geometric distance" and "spectral geometric distance" to reveal the affinity between pixels. Both the spatial geometric homogeneity of hyperspectral vectors in a local region, and the geometry manifold structure in the spectral domain, are explored to formulate spatial-spectral manifold regularizer for NMF. Some experiments are taken on some synthetic data and real hyperspectral data to investigate the performance of GNMF, and the results show that it can present state-of-the-art unmixing results.

show abstract

“…Endmember extraction are discussed in [25] with maximum simplex volume criterion and in [26] using ant colony optimization. Abundance estimation with a kernel weighted least squares method is developed in [27].…”

Section: Spectral Mixture Analysismentioning

confidence: 99%

Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications

Zhang

et al. 2013

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Fast Implementation of Maximum Simplex Volume-Based Endmember Extraction in Original Hyperspectral Data Space

Cited by 21 publications

References 25 publications

Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery

Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing

Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications

Contact Info

Product

Resources

About