Abstract
Abstract. While many spectral transformation techniques have been applied on spectral mixture analysis (SMA), few study examined their necessity and applicability. This paper focused on exploring the difference between spectrally transformed schemes and untransformed scheme to find out which transformed scheme performed better in SMA. In particular, nine spectrally transformed schemes as well as untransformed scheme were examined in two study areas. Each transformed scheme was tested 100 times using different endmember classes’ spectra under the endmember model of vegetation- high albedo impervious surface area-low albedo impervious surface area-soil (V-ISAh-ISAl-S). Performance of each scheme was assessed based on mean absolute error (MAE). Statistical analysis technique, Paired-Samples T test, was applied to test the significance of mean MAEs’ difference between transformed and untransformed schemes. Results demonstrated that only NSMA could exceed the untransformed scheme in all study areas. Some transformed schemes showed unstable performance since they outperformed the untransformed scheme in one area but weakened the SMA result in another region.
Highlights
Spectral mixture analysis (SMA) is majorly applied to estimate land cover class fractions from remotely sensed data, especially for medium and coarse spatial resolution imageries
We have examined nine transformed schemes including derivative analysis (DA) (Tsai and Philpot, 1998), principal component analysis (PCA) (Richards and Richards, 1999), independent components analysis (ICA) (Hyvarinen, 1999; Hyvärinen and Oja, 2000), Minimum Noise Fraction (MNF) (Boardman and Kruse, 1994; Green et al, 1988), Tasseled Cap (TC) (Kauth and Thomas, 1976), normalized spectral mixture analysis (NSMA) (Wu, 2004), and Tie spectral (Tie) transformation (Asner and Lobell (2000))
This study examined the performance of nine linearly and nonlinearly spectral transformations in Janesville and Asheville respectively by comparing their mean absolute error (MAE) with untransformed scheme
Summary
Spectral mixture analysis (SMA) is majorly applied to estimate land cover class fractions from remotely sensed data, especially for medium and coarse spatial resolution imageries. It assumes that a mixed pixel in a remote sensing image is comprised by several pure land cover types (endmembers). Mean absolute error (MAE) is used to assess the unmixing accuracy by calculating the absolute difference between the estimated and reference fractions of corresponding land cover class. It can be expressed using equation (2).
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