Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor
2015 (English)In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, Vol. 103Article in journal (Refereed) Published
With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.
Place, publisher, year, edition, pages
2015. Vol. 103
Spaceborne, SAR, ENVISAT ASAR, Urban Mapping, 30m Resolution, Spatial Indices, GLCM, Textures, Mountain Mask
Other Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-147154DOI: 10.1016/j.isprsjprs.2014.08.004ISI: 000353734600003OAI: oai:DiVA.org:kth-147154DiVA: diva2:727751
Updated from accepted to published.
QC 201506122014-06-232014-06-232015-06-12Bibliographically approved