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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
Identifiers
URN: urn:nbn:se:kth:diva-190506DOI: 10.1080/01431161.2015.1054050ISI: 000379552700005ScopusID: 2-s2.0-84935490827OAI: oai:DiVA.org:kth-190506DiVA: diva2:953604
Note

QC 20160818

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

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