Multidimensional SAR satellite images - a mapping perspective

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Karjalainen, Mika
dc.date.accessioned 2012-08-24T11:20:37Z
dc.date.available 2012-08-24T11:20:37Z
dc.date.issued 2010
dc.identifier.isbn 978-951-711-281-9 (electronic)
dc.identifier.isbn 978-951-711-280-2 (printed) #8195;
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/4785
dc.description.abstract In this thesis, the use of Synthetic Aperture Radar (SAR) satellite images in potential mapping applications areas in Finland was studied. SAR is an active sensor using the microwave region of the electromagnetic spectrum in its pulses. Microwaves penetrate clouds, smoke and dust without noticeable attenuation, enabling all-weather and night-and-day satellite imaging. Because cloudiness is very common in Finland, SAR is of importance for monitoring purposes. Recently, the number of SAR satellites has increased notably. First, SAR images can now be acquired more frequently than before. Second, SAR images can be acquired in multiple polarization channels, different frequency bands and various imaging geometries using several satellites. As the dimensionality of SAR data increases, it can be expected that more automatic and sophisticated processes are needed. The objective was to study streamlining of mapping processes based on SAR satellite images. First, automatic matching of remote sensing images and existing vector maps was studied. Second, multidimensional SAR satellite images were used in selected mapping applications. Example cases include agricultural monitoring (dual-polarimetric Envisat ASAR), detection of buildings (Radarsat-1 SAR), detection and verification of building subsidence (ERS-1 and ERS-2 SAR), and forest biomass mapping (ALOS PALSAR). The results showed that it may be possible to use existing vector maps to refine the geocoding parameters of SAR images. According to the ground check points, accuracy of around 2 pixels was achieved in the image-to-map co-registration. When Persistent Scatterers SAR Interferometry (PSI) subsidence rates of individual buildings were compared with the levelling measurements, RMSE of 0.82 mm/year was achieved. At its best R² values of 0.55 and 0.72 were obtained for crop biomass and forest above ground volume estimations respectively. Crop species were classified with the overall accuracy of 75%. Building detection percentages varied between 13% and 98%, depending on the orientation of the building wall and building height. The author believes that the results might show commercial potential and have a socioeconomic impact, providing the prices of SAR images decrease. PSI could be used to monitor subsidence of urban areas operationally. SAR satellite is the only way to monitor wide agricultural areas in Finland, even though the results are somewhat poor. In the case of forests, SAR enables more frequent update of forest information when compared to airborne laser scanning. SAR images might have potential in detection of changes in urban areas, especially in the remote areas of the world. The future of SAR satellites appears promising because more satellite systems will be launched in the next ten years. en
dc.format.extent Verkkokirja (3856 KB, 54 s.)
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Geodeettinen laitos en
dc.relation.ispartofseries Suomen geodeettisen laitoksen julkaisuja, 142 en
dc.relation.haspart [Publication 1]: Mika Karjalainen, Juha Hyyppä, and Risto Kuittinen. 2006. Determination of exterior orientation using linear features from vector maps. The Photogrammetric Record, volume 21, number 116, pages 329-341. © 2006 by authors. en
dc.relation.haspart [Publication 2]: Mika Karjalainen. 2007. Geocoding of synthetic aperture radar images using digital vector maps. IEEE Geoscience and Remote Sensing Letters, volume 4, number 4, pages 616-620. © 2007 Institute of Electrical and Electronics Engineers (IEEE). By permission. en
dc.relation.haspart [Publication 3]: Mika Karjalainen, Harri Kaartinen, and Juha Hyyppä. 2008. Agricultural monitoring using Envisat alternating polarization SAR images. Photogrammetric Engineering & Remote Sensing, volume 74, number 1, pages 117-126. © 2008 American Society for Photogrammetry and Remote Sensing (ASPRS). By permission. en
dc.relation.haspart [Publication 4]: Mika Karjalainen, Juha Hyyppä, and Yannick Devillairs. 2003. Urban change detection in the Helsinki metropolitan region using Radarsat-1 fine beam SAR images. In: Proceedings of the 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (URBAN 2003). Berlin, Germany. 22-23 May 2003. Pages 273-277. © 2003 Institute of Electrical and Electronics Engineers (IEEE). By permission. en
dc.relation.haspart [Publication 5]: Kirsi Karila, Mika Karjalainen, and Juha Hyyppä. 2005. Urban land subsidence studies in Finland using synthetic aperture radar images and coherent targets. The Photogrammetric Journal of Finland, volume 19, number 2, pages 43-53. © 2005 Finnish Society of Photogrammetry and Remote Sensing (FSPRS). By permission. en
dc.relation.haspart [Publication 6]: Mika Karjalainen, Ulla Pyysalo, Kirsi Karila, and Juha Hyyppä. 2009. Forest biomass estimation using ALOS PALSAR images in challenging natural forest area in Finland. In: Proceedings of ALOS PI 2008 Symposium. Island of Rhodes, Greece. 3-7 November 2008. ESA Special Publication SP-664. CD-ROM. 5 pages. © 2009 by authors. en
dc.subject.other Geoinformatics
dc.subject.other Electrical engineering
dc.title Multidimensional SAR satellite images - a mapping perspective en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Aalto-yliopiston teknillinen korkeakoulu fi
dc.contributor.school Insinööritieteiden ja arkkitehtuurin tiedekunta fi
dc.contributor.department Department of Surveying en
dc.contributor.department Maanmittaustieteiden laitos fi
dc.subject.keyword synthetic aperture radar en
dc.subject.keyword mapping en
dc.subject.keyword multidimensional data analysis en
dc.subject.keyword geocoding en
dc.identifier.urn URN:ISBN:978-951-711-281-9
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en


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