Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE

By Philip E. Dennison, Dar A. Roberts. 2003

473 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/f8393b83a62c6ea5efe12766322315f56fc66cd4

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In Ecology

In Global and Planetary Change

In Media Technology

Citations in the corpus

Using Imaging Spectroscopy to Study Ecosystem Processes and Properties (2004, background): “The primary and secondary effects of water content on leaf reflectance are greatest in spectral bands centered at 1450, 1940, and 2500 nm (Carter 1991); with indirect or secondary effects at 400 nm; in the red edge at 700 nm (Filella and Peñuelas 1994); and with NDVI.”
A survey of image classification methods and techniques for improving classification performance (2007, unknown): “…the approaches (Smith et al. 1990, Adams et al. 1993, Roberts et al. 1993, Settle and Drake 1993, Bateson and Curtiss 1996, Tompkins et al. 1997, Garcia-Haro et al. 1999, Mustard and Sunshine 1999, Van der Meer 1999, Maselli 2001, Dennison and Roberts 2003, Theseira et al. 2003, Small 2004).”
A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper (2004, methodology): “MESMA has been extensively used for mapping vegetation species in Southern California chaparral (Roberts et al., 1998; Dennison & Roberts, 2003a).”
Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data (2011, background): “…of Nebraska - Lincoln: https://digitalcommons.unl.edu/ nasapub/88 Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data M.A. Schull a,n, Y. Knyazikhin a, L. Xu a, A. Samanta a, P.L. Carmona b, L. Lepine c, J.P. Jenkins c, S.…”
Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models (2010, methodology): “To circumvent these issues, several alternatives have been developed that involve various endmember selection strategies (Carpenter et al., 1999; Dennison & Roberts, 2003; Miao et al., 2006; Okin et al., 2001; Plaza et al., 2004; Roberts et al., 1992, 1998; Smith et al., 1994.”
Differentiating plant species within and across diverse ecosystems with imaging spectroscopy (2015, methodology): “WithMESMA, a user is able to specify anRMSE threshold, and, if no training spectrum fits a pixel well, this pixel will be labeled ‘unclassified.’”
Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data (2012, methodology): “Two endmember MESMA (one shade endmember and one non-shade endmember) accounts for variability in reflectance, and can be used as a classification algorithm where the non-shade endmembers are assigned to specific classes (e.g. Dennison & Roberts, 2003a, 2003b).”