Mapping urban land cover types using object-based multiple endmember spectral mixture analysis

Caiyun Zhang, Hannah Cooper, Donna Selch, Xuelian Meng, Fang Qiu, Soe Myint, Charles Roberts, Zhixiao Xie

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Spectral mixture analysis has been frequently applied in various fields to solve the mixed pixel problem in remote sensing. So far, all the research in mixture analysis has focused on the sub-pixel analysis, i.e., selecting endmembers and conducting mixture analysis at the pixel level. Research efforts in mixture analysis at the object level are very scarce, even though the object-based image analysis (OBIA) techniques have been well developed. In this study, we examined the applicability of object-based mixture analysis in an urban environment using a Landsat Thematic Mapper image. Informative and accurate object-based fraction maps (vegetation, impervious surface, and water) were produced by combining the OBIA and multiple endmember spectral mixture analysis (MESMA) techniques. A new approach to identifying the spectral representatives of a specific class for MESMA was developed. The accuracy of the object-based fraction maps were assessed using manual interpretation results of a 1-m digital aerial photograph. Object-based mixture analysis produced a higher accuracy than traditional pixel-based mixture analysis. This work illustrates the potential of object-based mixture analysis of moderate spatial resolution imagery in mapping heterogeneous urban environments.

Original languageEnglish (US)
Pages (from-to)521-529
Number of pages9
JournalRemote Sensing Letters
Issue number6
StatePublished - Jun 3 2014

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Electrical and Electronic Engineering


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