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Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale
Book Contribution - Book Chapter Conference Contribution
The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments
with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which
typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods
that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of
Vegetation–Impervious–Soil urban land cover is presented, using medium resolution multispectral satellite imagery,
hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface
reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries
developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First
the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then
optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are
synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression
models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level
(mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute
Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to
the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.
with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which
typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods
that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of
Vegetation–Impervious–Soil urban land cover is presented, using medium resolution multispectral satellite imagery,
hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface
reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries
developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First
the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then
optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are
synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression
models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level
(mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute
Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to
the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.
Book: Remote Sensing Technologies and Applications in Urban Environments, Proceedings of SPIE
Volume: 10008
Pages: 1-13
Number of pages: 13
Publication year:2016
Keywords:urban land cover, multispectral satellite imagery, hyperspectral endmember libraries, synthetic mixing, support vector regression
Authors:International