Next Article in Journal
Multi-View Stereo Matching Based on Self-Adaptive Patch and Image Grouping for Multiple Unmanned Aerial Vehicle Imagery
Previous Article in Journal
Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests
Previous Article in Special Issue
Innovative Technologies for Terrestrial Remote Sensing
Article Menu

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2016, 8(2), 88; doi:10.3390/rs8020088

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

1
School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK
2
Department of Geography, Edge Hill University, St Helens Road, Ormskirk, Lancashire L39 4QP, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri and Prasad S. Thenkabail
Received: 30 September 2015 / Revised: 23 December 2015 / Accepted: 19 January 2016 / Published: 23 January 2016
View Full-Text   |   Download PDF [3298 KB, uploaded 23 January 2016]   |  

Abstract

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. View Full-Text
Keywords: urban; land cover; classification; WorldView-2; spatial resolution; spectral band; SVM; OBIA; accuracy; McNemar test urban; land cover; classification; WorldView-2; spatial resolution; spectral band; SVM; OBIA; accuracy; McNemar test
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Momeni, R.; Aplin, P.; Boyd, D.S. Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach. Remote Sens. 2016, 8, 88.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top