Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multitemporal radarsat-2 polarimetric SAR data for urban land-cover mapping
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
2010 (English)In: 100 Years ISPRS Advancing Remote Sensing Science, PT 1, 2010, Vol. 38, 175-180 p.Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this research is to evaluate multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using a novel classification scheme. Six-date RADARSAT-2 Polarimetric SAR data in both ascending and descending orbits were acquired during June to September 2008 in the rural-urban fringe of the Greater Toronto Area. The major land-cover types are builtup areas, roads, golf courses, forest, water and several types of agricultural crops. In this research, the different urban land-cover types and their corresponding polarimetric behaviors were studied. The polarimetric signatures of the various urban land-cover types were extracted from the RADARSAT-2 SAR images and analyzed using a new hierarchical multitemporal classification method. The results showed that the new classification method yielded high classification accuracy, with overall accuracy of 82.1% and Kappa coefficient 0.80 for the major 11 land-cover classes. The classification scheme can effectively extract the urban structures by mapping urban related classes such as streets and major roads with the higher user's accuracy, which is difficult to achieve using a single-date data.

 

Place, publisher, year, edition, pages
2010. Vol. 38, 175-180 p.
Series
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, ISSN 2194-9034
Keyword [en]
Land cover, classification, SAR, hierarchical, multitemporal
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-31424ISI: 000339410100030Scopus ID: 2-s2.0-79957643144OAI: oai:DiVA.org:kth-31424DiVA: diva2:403769
Conference
ISPRS Technical Commission VII Symposium on Advancing Remote Sensing Science; Vienna; Austria; 5 July 2010 through 7 July 2010
Note

QC 20110315

Available from: 2011-03-15 Created: 2011-03-15 Last updated: 2015-06-11Bibliographically approved
In thesis
1. Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping
Open this publication in new window or tab >>Multitemporal Spaceborne Polarimetric SAR Data for Urban Land Cover Mapping
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand.

 

This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study.

 

Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm.

 

Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2011. viii, 54 p.
Series
Trita-SOM , ISSN 1653-6126 ; 2011-05
Keyword
RADARSAT-2, spaceborne, polarimetric SAR, urban land cover, classification
National Category
Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-31176 (URN)978-91-7415-909-7 (ISBN)
Presentation
2011-03-16, Seminarierum 4055, KTH, Drottning Kristinas väg 30, Stockholm, 14:17 (English)
Opponent
Supervisors
Note

QC 20110315

Available from: 2011-03-15 Created: 2011-03-10 Last updated: 2013-12-04Bibliographically approved

Open Access in DiVA

No full text

Scopus

Search in DiVA

By author/editor
Niu, XinBan, Yifang
By organisation
Geoinformatics (closed 20110301)
Remote Sensing

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 114 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf