A Comparison of Spectral Mixture Analysis Methods for Urban Landscape Using Landsat Etm+ Data: Los Angeles, Ca

Although spectral mixture analysis has been widely used for mapping the abundances of physical components of urban surface with moderate spatial resolution satellite imagery recently, the spectral heterogeneity of urban land surface has still posed a great challenge to accurately estimate fractions of surface materials within a pixel. How to dealing with the highly spectral heterogeneous nature of urban land surface remains a scientific question. In this study, a comparison of different spectral mixture models was carried out to examine the performance of each model in dealing with spectral variability of urban surface. The comparison is focused on spectral normalized models and multiple endmember spectral mixture analysis (MESMA). Two spectral normalization algorithms, mean normalization and hyperspheric direction cosine (HSDC) normalization, were applied to Landsat ETM+ data acquired over Los Angeles, CA. A total of 170 spectral mixture models of two, three, and four endmembers were employed with MESMA. The reference data digitized from Arc2Earth was used to evaluated the mapping results. The results showed that MESMA is a promising tool to map abundances of urban surface components. Relative high mapping accuracies were achieved for vegetations and impervious surfaces. R2 and Root mean square error (RMSE) of vegetation fraction are 0.79 and 7.1%, respectively. The estimation of impervious surfaces obtained similar accuracy, with R2 0.72 and RMSE 10.7%. Both mean and HSDC normalized models made a notable improvement in mapping vegetation fraction and slight improvement in mapping impervious surface fractions, comparing with the standard SMA. Both two normalizations can only suppress spectral variation with similar spectral shape. HSDC is slightly better than mean normalization in reducing the effects of illumination and spectral variability of urban land surface.