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Remote Sens. 2017, 9(6), 565; doi:10.3390/rs9060565

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

1
Division of Forest, Nature and Landscape, KU Leuven, Leuven 3001, Belgium
2
Geography Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany
3
Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), Mol 2400, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: James Campbell and Prasad S. Thenkabail
Received: 24 April 2017 / Revised: 19 May 2017 / Accepted: 31 May 2017 / Published: 6 June 2017
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Abstract

Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the “Automated MUsic and spectral Separability based Endmember Selection technique” (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows. View Full-Text
Keywords: endmember selection; spectral library reduction; MUSIC; IES; MESMA; land cover fractions; mapping; hyperspectral remote sensing endmember selection; spectral library reduction; MUSIC; IES; MESMA; land cover fractions; mapping; hyperspectral remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Degerickx, J.; Okujeni, A.; Iordache, M.-D.; Hermy, M.; van der Linden, S.; Somers, B. A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover. Remote Sens. 2017, 9, 565.

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