Sub-pixel mapping of urban green space using multiple endmember spectral mixture analysis of EO-1 Hyperion data

Abstract

Urban green space is an important biophysical component in assessing urban environment. Remote sensing technology offers an alternative method to traditional ground-based survey of these green spaces. However, accurate green space extraction is still a challenge due to the existence of mixed pixels. Tradional methods such as classification and NDVI for deriving green space are found to be inaccurate and unsatisfactory. Multiple endmember spectral mixture analysis (MESMA) models spectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis, which is a technique for identifying materials in a hyperspectral image using endmembers from a spectral library.