EXTENDED QMODEL?objective definition of external end members in the analysis of mixtures

Full, William E.; Ehrlich, Robert; Klovan, J. E.

doi:10.1007/bf01031518

Cited by 146 publications

(59 citation statements)

References 8 publications

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“…In fact, Imbrie's Q-mode analysis and the subsequent QMODEL by Klovan and Miesch [30] are conceptually identical to vertex or pure pixel search, although the methodology is different. Likewise, Full et al already considered the same simplex volume minimization principle as Craig's in the 1980s [31]. CG has also been independently discovered in other fields such as chemometrics [32] and SP [33], [34].…”

Section: Who Discovered Convex Geometry For Blind Unmixing?mentioning

confidence: 99%

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Bioucas-Dias

Chan

et al. 2014

IEEE Signal Process. Mag.

411

322

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Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]- [7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing-blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts-discovered by early remote sensing researchers through empirical observations [3]- [7] and refined by later research-are elegant and very different from statistical independence-based BSS approaches established in

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Section: Who Discovered Convex Geometry For Blind Unmixing?mentioning

confidence: 99%

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Bioucas-Dias

Chan

et al. 2014

IEEE Signal Process. Mag.

411

322

View full text Add to dashboard Cite

show abstract

“…These end-members are described as a smooth grain shape or type, a second type is highly irregular, and a third which is intermediate in shape. After we determined statistically that there are three major grain shapes or types, the data were "unmixed" using a QMODEL unmixing algorithm (Full et al, 1982;Full et al, 1981;Klovan and Meisch, 1976). This algorithm gives the proportion of each end-member or grain shape in each sample.…”

Section: Methods and Proceduresmentioning

confidence: 99%

Provenance Changes of Fine Quartz Sands in Glacial and Interglacial Intervals of the Feni and Gardar Drifts, North Atlantic: Fourier Shape Analysis

Eggers¹,

Ehrlich²

1987

Initial Reports of the Deep Sea Drilling Project

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Using a combination of Fourier shape analysis and scanning electron microscopy, very fine quartz sand grains from sediments of the Feni and Gardar drifts (Holes 610A and 611A) were analyzed to obtain provenance information. Three grain populations were defined: (1) smooth: grains covered with fine-grained silica plastering, interpreted as weathered from continental bedrock; (2) intermediate: angular, freshly fractured grains, probably transported by glaciers and icerafting; and (3) irregular: grains covered with irregular, platy silica overgrowths, likely the products of submarine erosion of fine-grained marine sediments. Examination of the proportions of each grain type in samples from intervals interpreted to be glacial (dark, calcareous muds) and interglacial (light-colored calcareous oozes) showed that there are changes in provenance across the boundaries between these intervals. Proportions of each grain type vary widely within glacial intervals, but remain relatively invariant in inter glacials. This suggests that factors affecting provenance, such as the amount of sea ice, water depth on the continental shelf, and strength and direction of bottom currents, are more variable during glacial periods. Presence of the irregular grain type indicates that erosion and redeposition of marine sediments is a major source of sediment in sediment drifts in the deep sea.

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“…Thus, in the present paper the rotation technique (Figure 2d) proposed by Leinen and Pisias (1984) combined with an extended Q-mode factor analysis (Miesch, 1976) is used to determine chemically reasonable end-member compositions from the MM data set. This rotation scheme does not require the hypothesis of having sampled pure end-members (Full et al, 1981), but does assume true end-member compositions lie between the composition identified by Q-mode factor analysis (which forms a set of orthogonal axes) and the best known statistical parameter within a data set, i.e. the vector of mean composition (Aboul-Kassim, 1998;Aboul-Kassim and Simoneit, 2001;Aboul-Kassim and Williamson, 2002;Leinen and Pisias;1984).…”

Section: Bepmentioning

confidence: 99%

“…Several studies used multivariate analysis based on bottom sediments data sets to determine quantitatively the contribution of each contaminant from various sediment sources, (Aboul-Kassim and Simoneit, 2001;Dymond, 1981;Full et al, 1981;Grant, 1990;Imbrie and Van Andel, 1971;Klovan and Imbrie, 1971;Malinowski, 1991;Miesch, 1976;Rapp, 1991;Tysklind et al, 1992). Within such studies, they generally assumed that sediments are made up of a few end-member components and that as long as the area and time interval being considered are reasonably restricted, the sources are constant in composition.…”

Section: Introductionmentioning

confidence: 99%

Forensic Analysis and Genetic Source Partitioning Model for Portland Harbor Contaminated Sediments

Aboul-Kassim

Williamson²

2003

J ENV INFORM

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EXTENDED QMODEL?objective definition of external end members in the analysis of mixtures

Cited by 146 publications

References 8 publications

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Provenance Changes of Fine Quartz Sands in Glacial and Interglacial Intervals of the Feni and Gardar Drifts, North Atlantic: Fourier Shape Analysis

Forensic Analysis and Genetic Source Partitioning Model for Portland Harbor Contaminated Sediments

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