Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar scale-invariant feature transform (PSIFT) descriptor is proposed for SAR image registr...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2017-07, Vol.14 (7), p.1101-1105 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Zeng, Lina Zhou, Deyun Liang, Junli Zhang, Kun |
| description | Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar scale-invariant feature transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance. |
| doi_str_mv | 10.1109/LGRS.2017.2698450 |
| format | Article |
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In this letter, a polar scale-invariant feature transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2017.2698450</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Adaptive optics ; Bins ; Distortion ; Feature descriptor ; Feature extraction ; Image registration ; Matching ; Methods ; Noise ; Noise prediction ; Orientation ; polar transform ; Radar ; Radar imaging ; Registration ; Robustness ; Rotation ; SAR (radar) ; scale-invariant feature transform (SIFT) ; Speckle ; Synthetic aperture radar ; synthetic aperture radar (SAR) image registration ; Transforms</subject><ispartof>IEEE geoscience and remote sensing letters, 2017-07, Vol.14 (7), p.1101-1105</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-4b9f43fafa5562b8421dbdfe3988285e130ec68f5a7240d89678c8171f2a14893</citedby><cites>FETCH-LOGICAL-c293t-4b9f43fafa5562b8421dbdfe3988285e130ec68f5a7240d89678c8171f2a14893</cites><orcidid>0000-0002-9647-1557</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7927702$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7927702$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zeng, Lina</creatorcontrib><creatorcontrib>Zhou, Deyun</creatorcontrib><creatorcontrib>Liang, Junli</creatorcontrib><creatorcontrib>Zhang, Kun</creatorcontrib><title>Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. In this letter, a polar scale-invariant feature transform (PSIFT) descriptor is proposed for SAR image registration. The novel descriptor is invariant to rotation, skipping the dominant orientation assignment. In PSIFT, a polar-transformed support region is adopted to calculate the gradient magnitudes and orientations and further sampled in the radial and angular directions with different scales. The final descriptor is then built with the orientation bins covering the omnidirectional space. Furthermore, an improved dual-matching method is proposed to achieve sufficiently correct matches. Extensive experiments confirm that the PSIFT descriptor is suitable for SAR image registration because of its excellent performance.</description><subject>Adaptive optics</subject><subject>Bins</subject><subject>Distortion</subject><subject>Feature descriptor</subject><subject>Feature extraction</subject><subject>Image registration</subject><subject>Matching</subject><subject>Methods</subject><subject>Noise</subject><subject>Noise prediction</subject><subject>Orientation</subject><subject>polar transform</subject><subject>Radar</subject><subject>Radar imaging</subject><subject>Registration</subject><subject>Robustness</subject><subject>Rotation</subject><subject>SAR (radar)</subject><subject>scale-invariant feature transform (SIFT)</subject><subject>Speckle</subject><subject>Synthetic aperture radar</subject><subject>synthetic aperture radar (SAR) image registration</subject><subject>Transforms</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFKAzEQhoMoWKsPIF4WPG_NZJNNcizF1kJBaSt6C9PdpG5pd2uSCn17d23xMvMfvn8GPkLugQ4AqH6aTeaLAaMgByzXigt6QXoghEqpkHDZZS5SodXnNbkJYUMp40rJHvl4a7bok0WBW5tO6x_0FdYxGVuMB2-Tpcc6uMbvknYki2Mdv2ysimS4t_4PmGPZ1qc7XLfZrqsQPcaqqW_JlcNtsHfn3Sfv4-fl6CWdvU6mo-EsLZjOYspX2vHMoUMhcrZSnEG5Kp3NtFJMCQsZtUWunEDJOC2VzqUqFEhwDIErnfXJ4-nu3jffBxui2TQHX7cvDWjIBNeaZS0FJ6rwTQjeOrP31Q790QA1nT_T-TOdP3P213YeTp3KWvvPS82kpCz7BcaBa_U</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Zeng, Lina</creator><creator>Zhou, Deyun</creator><creator>Liang, Junli</creator><creator>Zhang, Kun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9647-1557</orcidid></search><sort><creationdate>20170701</creationdate><title>Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration</title><author>Zeng, Lina ; Zhou, Deyun ; Liang, Junli ; Zhang, Kun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-4b9f43fafa5562b8421dbdfe3988285e130ec68f5a7240d89678c8171f2a14893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive optics</topic><topic>Bins</topic><topic>Distortion</topic><topic>Feature descriptor</topic><topic>Feature extraction</topic><topic>Image registration</topic><topic>Matching</topic><topic>Methods</topic><topic>Noise</topic><topic>Noise prediction</topic><topic>Orientation</topic><topic>polar transform</topic><topic>Radar</topic><topic>Radar imaging</topic><topic>Registration</topic><topic>Robustness</topic><topic>Rotation</topic><topic>SAR (radar)</topic><topic>scale-invariant feature transform (SIFT)</topic><topic>Speckle</topic><topic>Synthetic aperture radar</topic><topic>synthetic aperture radar (SAR) image registration</topic><topic>Transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zeng, Lina</creatorcontrib><creatorcontrib>Zhou, Deyun</creatorcontrib><creatorcontrib>Liang, Junli</creatorcontrib><creatorcontrib>Zhang, Kun</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zeng, Lina</au><au>Zhou, Deyun</au><au>Liang, Junli</au><au>Zhang, Kun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2017-07-01</date><risdate>2017</risdate><volume>14</volume><issue>7</issue><spage>1101</spage><epage>1105</epage><pages>1101-1105</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>Obtaining high accuracy in orientation assignment for Synthetic Aperture Radar (SAR) image registration is a great challenge because of the serious speckle noise and geometrical distortion. 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| subjects | Adaptive optics Bins Distortion Feature descriptor Feature extraction Image registration Matching Methods Noise Noise prediction Orientation polar transform Radar Radar imaging Registration Robustness Rotation SAR (radar) scale-invariant feature transform (SIFT) Speckle Synthetic aperture radar synthetic aperture radar (SAR) image registration Transforms |
| title | Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration |
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