Random Walks for Pansharpening in Complex Tight Framelet Domain
In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fu...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2019-07, Vol.57 (7), p.5121-5134 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Wang, Jingkai Yang, Xiaoyuan Zhu, Ridong |
| description | In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information. |
| doi_str_mv | 10.1109/TGRS.2019.2897010 |
| format | Article |
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In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2019.2897010</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Coefficients ; Complex tight framelet (CFT) ; Computational modeling ; graph representation ; Hidden Markov models ; hidden Markov tree ; Image edge detection ; Image enhancement ; Image fusion ; Markov chains ; multispectral (MS) image ; panchromatic (PAN) image ; pansharpening ; Probability ; Random walk ; random walks (RWs) ; Remote sensing ; Spatial resolution ; Transforms</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2019-07, Vol.57 (7), p.5121-5134</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-13851bb6af4af947be27382b556968b1d17a947f1d543375a3291d6fd04b19f53</citedby><cites>FETCH-LOGICAL-c293t-13851bb6af4af947be27382b556968b1d17a947f1d543375a3291d6fd04b19f53</cites><orcidid>0000-0001-5341-183X ; 0000-0002-7367-0115</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8672085$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8672085$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Jingkai</creatorcontrib><creatorcontrib>Yang, Xiaoyuan</creatorcontrib><creatorcontrib>Zhu, Ridong</creatorcontrib><title>Random Walks for Pansharpening in Complex Tight Framelet Domain</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information.</description><subject>Algorithms</subject><subject>Coefficients</subject><subject>Complex tight framelet (CFT)</subject><subject>Computational modeling</subject><subject>graph representation</subject><subject>Hidden Markov models</subject><subject>hidden Markov tree</subject><subject>Image edge detection</subject><subject>Image enhancement</subject><subject>Image fusion</subject><subject>Markov chains</subject><subject>multispectral (MS) image</subject><subject>panchromatic (PAN) image</subject><subject>pansharpening</subject><subject>Probability</subject><subject>Random walk</subject><subject>random walks (RWs)</subject><subject>Remote sensing</subject><subject>Spatial resolution</subject><subject>Transforms</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKs_QLwEPG_N5DsnkWqrUFBqxWPIdpN2637UZAv6793S4mlg3uedgQehayAjAGLuFtP5-4gSMCOqjSJATtAAhNAZkZyfokGfyKyP6Dm6SGlDCHABaoDu564p2hp_uuor4dBG_OaatHZx65uyWeGyweO23lb-By_K1brDk-hqX_kOP7a1K5tLdBZclfzVcQ7Rx-RpMX7OZq_Tl_HDLFtSw7oMmBaQ59IF7oLhKvdUMU1zIaSROocClOvXAQrBGVPCMWqgkKEgPAcTBBui28PdbWy_dz51dtPuYtO_tJRyLSVXjPYUHKhlbFOKPthtLGsXfy0Qu_dk957s3pM9euo7N4dO6b3_57VUlGjB_gBLNWIm</recordid><startdate>20190701</startdate><enddate>20190701</enddate><creator>Wang, Jingkai</creator><creator>Yang, Xiaoyuan</creator><creator>Zhu, Ridong</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>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5341-183X</orcidid><orcidid>https://orcid.org/0000-0002-7367-0115</orcidid></search><sort><creationdate>20190701</creationdate><title>Random Walks for Pansharpening in Complex Tight Framelet Domain</title><author>Wang, Jingkai ; Yang, Xiaoyuan ; Zhu, Ridong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-13851bb6af4af947be27382b556968b1d17a947f1d543375a3291d6fd04b19f53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Coefficients</topic><topic>Complex tight framelet (CFT)</topic><topic>Computational modeling</topic><topic>graph representation</topic><topic>Hidden Markov models</topic><topic>hidden Markov tree</topic><topic>Image edge detection</topic><topic>Image enhancement</topic><topic>Image fusion</topic><topic>Markov chains</topic><topic>multispectral (MS) image</topic><topic>panchromatic (PAN) image</topic><topic>pansharpening</topic><topic>Probability</topic><topic>Random walk</topic><topic>random walks (RWs)</topic><topic>Remote sensing</topic><topic>Spatial resolution</topic><topic>Transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jingkai</creatorcontrib><creatorcontrib>Yang, Xiaoyuan</creatorcontrib><creatorcontrib>Zhu, Ridong</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>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>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Jingkai</au><au>Yang, Xiaoyuan</au><au>Zhu, Ridong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Random Walks for Pansharpening in Complex Tight Framelet Domain</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2019-07-01</date><risdate>2019</risdate><volume>57</volume><issue>7</issue><spage>5121</spage><epage>5134</epage><pages>5121-5134</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2019.2897010</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-5341-183X</orcidid><orcidid>https://orcid.org/0000-0002-7367-0115</orcidid></addata></record> |
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| subjects | Algorithms Coefficients Complex tight framelet (CFT) Computational modeling graph representation Hidden Markov models hidden Markov tree Image edge detection Image enhancement Image fusion Markov chains multispectral (MS) image panchromatic (PAN) image pansharpening Probability Random walk random walks (RWs) Remote sensing Spatial resolution Transforms |
| title | Random Walks for Pansharpening in Complex Tight Framelet Domain |
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