Change search
ReferencesLink to record
Permanent link

Direct link
RADARSAT-2 fine-beam polarimetric and ultra-fine-beam SAR data for urban mapping: comparison and synergy
Natl Univ Def Technol, Peoples R China.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2016 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 37, no 12, 2810-2830 p.Article in journal (Refereed) PublishedText
Abstract [en]

The aim of this article is to investigate the capabilities of multitemporal RADARSAT-2 fine-beam polarimetric synthetic aperture radar (SAR) data and RADARSAT-2 ultra-fine-beam C-band single-polarization HH SAR (C-HH SAR) data for detailed urban land-cover mapping using a contextual approach. With an adaptive Markov random field and a spatially variant finite mixture model, contextual information was effectively explored to improve the mapping accuracy. A texture enhancement in FMM was further proposed to improve the classification accuracy. Moreover, a rule-based approach exploring object features and spatial relationships was employed to extract road, street, and park. Three-date RADARSAT-2 fine-beam polarimetric SAR (PolSAR) and three-date RADARSAT-2 ultra-fine-beam C-HH SAR data over the Greater Toronto area were used for the evaluation. For 10 major classes, the overall accuracy (OA) is 51% for C-HH SAR data and 79% for PolSAR data. Compared with C-HH SAR, PolSAR data produced better results for identifying various urban patterns. Although with multi-date, the C-HH SAR data showed low capability to distinguish high-density residential area and industry commercial area (Ind.). Considerable low-density residential area (LD) was misclassified as forest. Identification of the construction site (Cons.) and golf course were poor. Nevertheless, the efficiency of the multitemporal C-HH SAR textures for distinguishing the built-up areas was observed. By texture enhancement with the synergy of the PolSAR and C-HH SAR data, the mapping results could be significantly improved, especially for LD, forest, and crops. The OA is improved by 2.7% for PolSAR data, and 11.1% for C-HH SAR data. Road, street, and park could be extracted by the rule-based approach with OA about 77% for 13 classes.

Place, publisher, year, edition, pages
2016. Vol. 37, no 12, 2810-2830 p.
National Category
Remote Sensing
URN: urn:nbn:se:kth:diva-190506DOI: 10.1080/01431161.2015.1054050ISI: 000379552700005ScopusID: 2-s2.0-84935490827OAI: diva2:953604

QC 20160818

Available from: 2016-08-18 Created: 2016-08-12 Last updated: 2016-08-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Niu, XinBan, Yifang
By organisation
In the same journal
International Journal of Remote Sensing
Remote Sensing

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 2 hits
ReferencesLink to record
Permanent link

Direct link