Abstract
Original language | English |
---|---|
Pages (from-to) | 1-23 |
Journal | Remote Sensing |
Volume | 8 |
Issue number | 2 |
Early online date | 23 Jan 2016 |
DOIs | |
Publication status | E-pub ahead of print - 23 Jan 2016 |
Keywords
- urban
- land cover
- classification
- WorldView-2
- spatial resolution
- spectral band
- SVM
- OBIA
- accuracy
- McNemar test
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In: Remote Sensing, Vol. 8, No. 2, 23.01.2016, p. 1-23.
Research output: Contribution to journal › Article (journal) › peer-review
TY - JOUR
T1 - Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach
AU - Momeni, Rahman
AU - Aplin, Paul
AU - Boyd, Doreen S.
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PY - 2016/1/23
Y1 - 2016/1/23
N2 - Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.
AB - Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.
KW - urban
KW - land cover
KW - classification
KW - WorldView-2
KW - spatial resolution
KW - spectral band
KW - SVM
KW - OBIA
KW - accuracy
KW - McNemar test
U2 - 10.3390/rs8020088
DO - 10.3390/rs8020088
M3 - Article (journal)
SN - 2072-4292
VL - 8
SP - 1
EP - 23
JO - Remote Sensing
JF - Remote Sensing
IS - 2
ER -