Urban landcover mapping using different Spectral Mixture Analysis methods

The complex spatial and spectral variability of urban structures present fundamental challenges to deriving accurate remote sensing information for urban areas. Spectral mixture analysis (SMA), based on a physical mixture model, has ability to extract sub-pixel information such as the abundances of each endmember presented in the pixel (image unity). In this paper, different spectral mixture methods have been applied in order to examine the performance of each model in dealing with spectral variability of urban surface. The comparison is focused on linear spectral mixture analysis (LSMA) which is using a fixed number of endmembers for the entire scene and multiple endmember spectral mixture analysis (MESMA) which allows the number and types of endmembers to vary from pixel to pixel to extract the abundances of urban surface components. These techniques have been applied to map the physical components of urban land cover for the city of Bucharest, Romania, using Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and IKONOS imagery for 1989 - 2007 period. This paper demonstrates the potential of moderate-and high resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.