Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon
Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requ...
Gespeichert in:
| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | IEEE geoscience and remote sensing letters |
| container_volume | 19 |
| creator | Kuck, Tahisa N. Gomez, Luis D. Sano, Edson E. Bispo, Polyanna da C. Honorio, Douglas D. C. |
| description | Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the \alpha \beta estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative. |
| doi_str_mv | 10.1109/LGRS.2021.3057263 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_LGRS_2021_3057263</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9360216</ieee_id><sourcerecordid>2610182848</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-a488e926b284ddb8033791abc2e9a529b7852f259e6e4cf1d27485b851a4a9c53</originalsourceid><addsrcrecordid>eNo9kEtPwkAUhSdGExH9AcbNJK6L8-i0M0skgiQQiNXE3WTa3mqhD5wpC_j1TANxde_N_c45yUHokZIRpUS9LGYfyYgRRkeciJhF_AoNqBAy8Be97vdQBELJ71t059yGEBZKGQ_Qeg22aG1tmgxwW-BkB9m2Ajwtqw6sw_6HJ6tkuQqS7WEJOZ7X5gccntq2xt0v4FdrjmVVmgaPa3Nsm3t0U5jKwcNlDtHX9O1z8h4sVrP5ZLwIMqZ4FxgfD4pFKZNhnqeScB4ratKMgTKCqTSWghVMKIggzAqasziUIpWCmtCoTPAhej777mz7twfX6U27t42P1CyihEpvLD1Fz1RmW-csFHpny9rYg6ZE98XpvjjdF6cvxXnN01lTAsA_r3jkoYifAO4vaA4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2610182848</pqid></control><display><type>article</type><title>Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon</title><source>IEEE Electronic Library (IEL)</source><creator>Kuck, Tahisa N. ; Gomez, Luis D. ; Sano, Edson E. ; Bispo, Polyanna da C. ; Honorio, Douglas D. C.</creator><creatorcontrib>Kuck, Tahisa N. ; Gomez, Luis D. ; Sano, Edson E. ; Bispo, Polyanna da C. ; Honorio, Douglas D. C.</creatorcontrib><description>Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the <inline-formula> <tex-math notation="LaTeX">\alpha \beta </tex-math></inline-formula> estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2021.3057263</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Backscatter ; Deforestation ; Detection ; Diffusion rate ; Edge detection ; Fans ; Filters ; Forestry ; Image edge detection ; Image filters ; Indexes ; Information processing ; Inspection ; Performance evaluation ; Radar imaging ; Rainforests ; SAR images ; Speckle ; speckle filtering ; Superhigh frequencies ; Synthetic aperture radar ; Target detection ; Tropical climate ; tropical forest ; Tropical forests ; Visual inspection ; Wiener filtering ; X-band</subject><ispartof>IEEE geoscience and remote sensing letters, 2022, Vol.19, p.1-5</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-a488e926b284ddb8033791abc2e9a529b7852f259e6e4cf1d27485b851a4a9c53</citedby><cites>FETCH-LOGICAL-c293t-a488e926b284ddb8033791abc2e9a529b7852f259e6e4cf1d27485b851a4a9c53</cites><orcidid>0000-0001-5760-556X ; 0000-0002-2798-6223 ; 0000-0001-5878-6323 ; 0000-0003-0952-7055</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9360216$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9360216$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kuck, Tahisa N.</creatorcontrib><creatorcontrib>Gomez, Luis D.</creatorcontrib><creatorcontrib>Sano, Edson E.</creatorcontrib><creatorcontrib>Bispo, Polyanna da C.</creatorcontrib><creatorcontrib>Honorio, Douglas D. C.</creatorcontrib><title>Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the <inline-formula> <tex-math notation="LaTeX">\alpha \beta </tex-math></inline-formula> estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.</description><subject>Backscatter</subject><subject>Deforestation</subject><subject>Detection</subject><subject>Diffusion rate</subject><subject>Edge detection</subject><subject>Fans</subject><subject>Filters</subject><subject>Forestry</subject><subject>Image edge detection</subject><subject>Image filters</subject><subject>Indexes</subject><subject>Information processing</subject><subject>Inspection</subject><subject>Performance evaluation</subject><subject>Radar imaging</subject><subject>Rainforests</subject><subject>SAR images</subject><subject>Speckle</subject><subject>speckle filtering</subject><subject>Superhigh frequencies</subject><subject>Synthetic aperture radar</subject><subject>Target detection</subject><subject>Tropical climate</subject><subject>tropical forest</subject><subject>Tropical forests</subject><subject>Visual inspection</subject><subject>Wiener filtering</subject><subject>X-band</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtPwkAUhSdGExH9AcbNJK6L8-i0M0skgiQQiNXE3WTa3mqhD5wpC_j1TANxde_N_c45yUHokZIRpUS9LGYfyYgRRkeciJhF_AoNqBAy8Be97vdQBELJ71t059yGEBZKGQ_Qeg22aG1tmgxwW-BkB9m2Ajwtqw6sw_6HJ6tkuQqS7WEJOZ7X5gccntq2xt0v4FdrjmVVmgaPa3Nsm3t0U5jKwcNlDtHX9O1z8h4sVrP5ZLwIMqZ4FxgfD4pFKZNhnqeScB4ratKMgTKCqTSWghVMKIggzAqasziUIpWCmtCoTPAhej777mz7twfX6U27t42P1CyihEpvLD1Fz1RmW-csFHpny9rYg6ZE98XpvjjdF6cvxXnN01lTAsA_r3jkoYifAO4vaA4</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Kuck, Tahisa N.</creator><creator>Gomez, Luis D.</creator><creator>Sano, Edson E.</creator><creator>Bispo, Polyanna da C.</creator><creator>Honorio, Douglas D. C.</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/0000-0001-5760-556X</orcidid><orcidid>https://orcid.org/0000-0002-2798-6223</orcidid><orcidid>https://orcid.org/0000-0001-5878-6323</orcidid><orcidid>https://orcid.org/0000-0003-0952-7055</orcidid></search><sort><creationdate>2022</creationdate><title>Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon</title><author>Kuck, Tahisa N. ; Gomez, Luis D. ; Sano, Edson E. ; Bispo, Polyanna da C. ; Honorio, Douglas D. C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-a488e926b284ddb8033791abc2e9a529b7852f259e6e4cf1d27485b851a4a9c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Backscatter</topic><topic>Deforestation</topic><topic>Detection</topic><topic>Diffusion rate</topic><topic>Edge detection</topic><topic>Fans</topic><topic>Filters</topic><topic>Forestry</topic><topic>Image edge detection</topic><topic>Image filters</topic><topic>Indexes</topic><topic>Information processing</topic><topic>Inspection</topic><topic>Performance evaluation</topic><topic>Radar imaging</topic><topic>Rainforests</topic><topic>SAR images</topic><topic>Speckle</topic><topic>speckle filtering</topic><topic>Superhigh frequencies</topic><topic>Synthetic aperture radar</topic><topic>Target detection</topic><topic>Tropical climate</topic><topic>tropical forest</topic><topic>Tropical forests</topic><topic>Visual inspection</topic><topic>Wiener filtering</topic><topic>X-band</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kuck, Tahisa N.</creatorcontrib><creatorcontrib>Gomez, Luis D.</creatorcontrib><creatorcontrib>Sano, Edson E.</creatorcontrib><creatorcontrib>Bispo, Polyanna da C.</creatorcontrib><creatorcontrib>Honorio, Douglas D. C.</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>Kuck, Tahisa N.</au><au>Gomez, Luis D.</au><au>Sano, Edson E.</au><au>Bispo, Polyanna da C.</au><au>Honorio, Douglas D. C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2022</date><risdate>2022</risdate><volume>19</volume><spage>1</spage><epage>5</epage><pages>1-5</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the <inline-formula> <tex-math notation="LaTeX">\alpha \beta </tex-math></inline-formula> estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2021.3057263</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-5760-556X</orcidid><orcidid>https://orcid.org/0000-0002-2798-6223</orcidid><orcidid>https://orcid.org/0000-0001-5878-6323</orcidid><orcidid>https://orcid.org/0000-0003-0952-7055</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1545-598X |
| ispartof | IEEE geoscience and remote sensing letters, 2022, Vol.19, p.1-5 |
| issn | 1545-598X 1558-0571 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_LGRS_2021_3057263 |
| source | IEEE Electronic Library (IEL) |
| subjects | Backscatter Deforestation Detection Diffusion rate Edge detection Fans Filters Forestry Image edge detection Image filters Indexes Information processing Inspection Performance evaluation Radar imaging Rainforests SAR images Speckle speckle filtering Superhigh frequencies Synthetic aperture radar Target detection Tropical climate tropical forest Tropical forests Visual inspection Wiener filtering X-band |
| title | Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T04%3A57%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Performance%20of%20Speckle%20Filters%20for%20COSMO-SkyMed%20Images%20From%20the%20Brazilian%20Amazon&rft.jtitle=IEEE%20geoscience%20and%20remote%20sensing%20letters&rft.au=Kuck,%20Tahisa%20N.&rft.date=2022&rft.volume=19&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.issn=1545-598X&rft.eissn=1558-0571&rft.coden=IGRSBY&rft_id=info:doi/10.1109/LGRS.2021.3057263&rft_dat=%3Cproquest_RIE%3E2610182848%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2610182848&rft_id=info:pmid/&rft_ieee_id=9360216&rfr_iscdi=true |