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Autoregressive model for multi-pass SAR change detection based on image stacks
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
Universidade Federal do Pampa, BRA.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.ORCID-id: 0000-0002-6643-312x
Vise andre og tillknytning
2018 (engelsk)Inngår i: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Bovolo F.,Bruzzone L., SPIE , 2018, Vol. 10789, artikkel-id 1078916Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications. © 2018 SPIE.

sted, utgiver, år, opplag, sider
SPIE , 2018. Vol. 10789, artikkel-id 1078916
Serie
Proceedings of SPIE, ISSN 0277-786X
Emneord [en]
AR models, Change detection, SAR, Time series, Image enhancement, Radar measurement, Remote sensing, Synthetic aperture radar, Auto regressive models, Change detection algorithms, Pixel position, Reference image, Research topics, Synthetic aperture radar (SAR) images, Radar imaging
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Identifikatorer
URN: urn:nbn:se:bth-17475DOI: 10.1117/12.2325661ISI: 000455305000036Scopus ID: 2-s2.0-85059005687ISBN: 9781510621619 (tryckt)OAI: oai:DiVA.org:bth-17475DiVA, id: diva2:1277040
Konferanse
Image and Signal Processing for Remote Sensing XXIV 2018, Berlin, 10 September 2018 through 12 September 2018
Tilgjengelig fra: 2019-01-09 Laget: 2019-01-09 Sist oppdatert: 2019-01-28bibliografisk kontrollert

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Vu, Viet ThuyPettersson, Mats

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