onr:"swepub:oai:DiVA.org:kth-39586"
Search: onr:"swepub:oai:DiVA.org:kth-39586" > Fusion of RADARSAT ...
- 1 of 1
- Previous record
- Next record
- To hitlist
Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection
-
- Ban, Yifang (author)
- KTH,Geoinformatik
-
- Hu, Hongtao (author)
- KTH,Geoinformatik
-
- Rangel, Irene (author)
- KTH,Geoinformatik
- (creator_code:org_t)
- SPIE, 2007
- 2007
- English.
- In: Geoinformatics 2007Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE. - 9780819469120 ; , s. H7522-H7522
- Conference paper (peer-reviewed)
Abstract
Subject headings
Close
- The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
Keyword
- QuickBird
- RADARSAT SAR
- land-cover mapping
- change detection
- texture analysis
- artificial neural network
- object-based classification
- data fusion
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
- Geoinformatics 2007Proceedings of SPIE - The International Society for Optical E... (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Ban, Yifang
- Hu, Hongtao
- Rangel, Irene
- About the subject
-
- ENGINEERING AND TECHNOLOGY
- ENGINEERING AND ...
- and Environmental En ...
- and Remote Sensing
- Articles in the publication
- Geoinformatics 2 ...
- By the university
- Royal Institute of Technology
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
