Mr. Tianxing Wang Profile

Tianxing Wang

at Beijing Normal Univ

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Publications (1)

Proceedings Article | 29 December 2008 Paper

Accuracy assessment of linear spectral mixture model due to terrain undulation

Tianxing Wang, Songlin Chen, Ya Ma

Proceedings Volume 7285, 72853R (2008) https://doi.org/10.1117/12.815017

KEYWORDS: Vegetation, Spectral models, Earth observing sensors, Spatial resolution, High resolution satellite images, Remote sensing, Image resolution, Reflectivity, Visualization, Analytical research

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Mixture spectra are common in remote sensing due to the limitations of spatial resolution and the heterogeneity of land surface. During the past 30 years, a lot of subpixel model have developed to investigate the information within mixture pixels. Linear spectral mixture model (LSMM) is a simper and more general subpixel model. LSMM also known as spectral mixture analysis is a widely used procedure to determine the proportion of endmembers (constituent materials) within a pixel based on the endmembers' spectral characteristics. The unmixing accuracy of LSMM is restricted by variety of factors, but now the research about LSMM is mostly focused on appraisement of nonlinear effect relating to itself and techniques used to select endmembers, unfortunately, the environment conditions of study area which could sway the unmixing-accuracy, such as atmospheric scatting and terrain undulation, are not studied. This paper probes emphatically into the accuracy uncertainty of LSMM resulting from the terrain undulation. ASTER dataset was chosen and the C terrain correction algorithm was applied to it. Based on this, fractional abundances for different cover types were extracted from both pre- and post-C terrain illumination corrected ASTER using LSMM. Simultaneously, the regression analyses and the IKONOS image were introduced to assess the unmixing accuracy. Results showed that terrain undulation could dramatically constrain the application of LSMM in mountain area. Specifically, for vegetation abundances, a improved unmixing accuracy of 17.6% (regression against to NDVI) and 18.6% (regression against to MVI) for R² was achieved respectively by removing terrain undulation. Anyway, this study indicated in a quantitative way that effective removal or minimization of terrain illumination effects was essential for applying LSMM. This paper could also provide a new instance for LSMM applications in mountainous areas. In addition, the methods employed in this study could be effectively used to evaluate different algorithms of terrain undulation correction for further study.

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