Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis

By C. Bateson, Gregory P. Asner, C. Wessman. IEEE Transactions on Geoscience and Remote Sensing, 2000

414 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d8c7b197b7a927e6adb7ad79921af1063ddd14cb

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Citations in the corpus

Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (2012, methodology): “This method is similar to the idea of endmember selection (Bateson et al. 2000; Somers et al. 2011).”
Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data (2011, background): “In an attempt to overcome this limitation, various techniques to address endmember variability and similarity have been developed (e.g., Asner & Lobell, 2000; Bateson et al., 2000; Roberts et al., 1998; Somers et al., 2009a,b).”
Spatial/spectral endmember extraction by multidimensional morphological operations (2002, methodology): “A set of endmembers and cover fractions is called a mixture model [16].”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “, [115]–[118]), including all statistical algorithms that treat endmembers as distributions.”
Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards (2009, background): “As a result, contrary to nonlinear mixing techniques numerous solutions are available to account for endmember variability based on iterative mixture analysis cycles (e.g., Asner & Lobell, 2000; Bateson et al., 2000; Roberts et al., 1998; Rogge et al., 2006), waveband selection algorithms (e.”
QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management (2008, background): “Bateson et al. (2000) used end-member bundles to account for the variation of end-members in extracting sub-pixel vegetation information in natural environments.”
The sequential maximum angle convex cone (SMACC) endmember model (2004, methodology): “The hyperspectral image was also expanded in channels rather than pixels, utilizing Equation (2).”
A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data (2004, methodology): “Table I shows an overview of the main characteristics of several available methods, including the manual endmember selection tool (MEST) [14] , pixel purity index (PPI) [15], N-FINDR [6], iterative error analysis (IEA) [16], optical real-time adaptative spectral identification system (ORASIS) [17], ”
Vertex component analysis: a fast algorithm to unmix hyperspectral data (2005, methodology): “The method presented in [35] is also of MVT type, but by introducing the notion of bundles, it takes into account the end-member variability usually present in hyperspectral mixtures.”
Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE (2003, background): “Bateson et al. (2000) grew endmember “bundles” within a simplex containing the image spectra.”
Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models (2010, result): “The result after aggregation is comparable to considering endmember bundles (Bateson et al., 2000).”
Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data (2012, background): “Potential endmember spectra can be collected from reference materials in a laboratory setting (e.g. Roberts et al., 1993), extracted from imagery (Bateson et al., 2000; Dennison & Roberts, 2003a), measured in the field (Herold et al., 2004; Roberts et al., 2004) or simulated with radiative transfer…”
Sparse Unmixing of Hyperspectral Data (2011, background): “ASC is, however, prone to strong criticisms because, in a real image, there is strong signature variability [37] that, at the very least, introduces positive scaling factors varying from pixel to pixel in the signatures present in the mixtures.”