Hyperspectral data analysis and supervised feature reduction via projection pursuit

Jiménez, Luís Ortiz; Landgrebe, D. A.

doi:10.1109/36.803413

Cited by 255 publications

(122 citation statements)

References 22 publications

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“…This projection technique was extended by three unconstrained least squares approaches [23] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability framework [24] and projection pursuit [25], [26] have also been applied to hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

Nascimento

Dias

2003

Pattern Recognition and Image Analysis

114

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Abstract-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 linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.Index Terms-Independent component analysis (ICA), independent factor analysis (IFA), mixture of Gaussians, unmixing hyperspectral data.

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Section: Introductionmentioning

confidence: 99%

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

Nascimento

Dias

2003

Pattern Recognition and Image Analysis

114

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“…A great deal of research in the PP community has been centered on the construction of meaningful PP indexes for different purposes. It is possible to find PP indexes for clustering analysis [42,19,38,39,43,30,13], for supervised analysis [44,45,21,5] and for regression analysis [46,47]. Given that this paper is targeted to supervised analysis, we briefly describe some relevant supervised PP indexes included in the experimental evaluation of the paper.…”

Section: Projection Pursuit Indicesmentioning

confidence: 99%

“…Though the collection of large p small n datasets is nowadays a common practice in many fields, their analysis and interpretation is still a challenging task [5,6,1]. This difficulty is mainly originated by the so-called "curse of dimensionality" phenomenon, inherent in such a kind of data [7].…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon states that as the dimensionality increases, the corresponding space becomes emptier and the data points tend to be equidistant. This generates detrimental impacts in most machine-learning and pattern-recognition methods (including model-estimation instability, model over fitting and local convergence), compromising the generalization performance and reliability of such methods [5,6].…”

Section: Introductionmentioning

confidence: 99%

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A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

et al. 2015

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“…The drawbacks of PCA and other measures with unclear physical interpretation are discussed in [8]. Jimenez [9] describes a preprocessing step for reducing the number of features. Two criteria for feature selection are discussed in [4]: the scatter separability (SS) criteria and the Maximum Likelihood (ML).…”

Section: Introductionmentioning

confidence: 99%

Target Segmentation in Scenes with Diverse Background

Grönwall

Tolt

2011

Image Analysis

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Abstract. We propose a target segmentation approach based on sensor data fusion that can deal with the problem of a diverse background. Features from sensor images, including data from a laser scanner and passive sensors (cameras), are analyzed using Gaussian mixture estimation. The approach tackles some of the difficulties with Gaussian mixtures, e.g., selecting the number of initial components and a good description of data in terms of the number of Gaussian components, and determining the relevant features for the current data set. The feature selection quality is analyzed on-line. We propose a criterion that determines the quality of the resulting clusters in terms of their respective spatial distribution. The output from the analysis is used for object-background segmentation. Segmentation examples of surface-laid mines in outdoor scenes are shown.

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Hyperspectral data analysis and supervised feature reduction via projection pursuit

Cited by 255 publications

References 22 publications

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

Target Segmentation in Scenes with Diverse Background

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