Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series
This paper presents a new method based on recent optimization technique to detect slow-moving landslides (
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2019-04, Vol.57 (4), p.2133-2144 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Pham, Mai Quyen Lacroix, Pascal Doin, Marie Pierre |
| description | This paper presents a new method based on recent optimization technique to detect slow-moving landslides ( |
| doi_str_mv | 10.1109/TGRS.2018.2871550 |
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Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed <inline-formula> <tex-math notation="LaTeX">\ell _{1,2} </tex-math></inline-formula>-norm is the most suitable norm for this detection problem, compared to pure <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">\ell _{2} </tex-math></inline-formula>-norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%.]]></description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2018.2871550</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Computer Science ; Correlation ; Data ; Data processing ; Detection ; Errors ; Field investigations ; Field tests ; Geophysics ; Image detection ; Image resolution ; Landslides ; Landslides & mudslides ; Mathematics ; Methods ; Optical imaging ; Optimization ; Optimization and Control ; Optimization techniques ; Outliers (statistics) ; Physical properties ; Physics ; Random noise ; Representations ; satellite image time-series ; Satellite imagery ; Satellites ; segmentation ; Signal and Image Processing ; slow-moving landslides ; sparsity ; Terrain factors ; Time series ; Time series analysis</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2019-04, Vol.57 (4), p.2133-2144</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c327t-bedca8ea6e7fbcc35e8b15188607b71d679784576f8e9cd0a8b1c0242246b5dd3</citedby><cites>FETCH-LOGICAL-c327t-bedca8ea6e7fbcc35e8b15188607b71d679784576f8e9cd0a8b1c0242246b5dd3</cites><orcidid>0000-0003-1482-0600 ; 0000-0002-9546-4005 ; 0000-0002-9974-2455</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8492350$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8492350$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-02062494$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Pham, Mai Quyen</creatorcontrib><creatorcontrib>Lacroix, Pascal</creatorcontrib><creatorcontrib>Doin, Marie Pierre</creatorcontrib><title>Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description><![CDATA[This paper presents a new method based on recent optimization technique to detect slow-moving landslides (<150m/year) in time series of displacement field generated by satellite images. Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed <inline-formula> <tex-math notation="LaTeX">\ell _{1,2} </tex-math></inline-formula>-norm is the most suitable norm for this detection problem, compared to pure <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">\ell _{2} </tex-math></inline-formula>-norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%.]]></description><subject>Computer Science</subject><subject>Correlation</subject><subject>Data</subject><subject>Data processing</subject><subject>Detection</subject><subject>Errors</subject><subject>Field investigations</subject><subject>Field tests</subject><subject>Geophysics</subject><subject>Image detection</subject><subject>Image resolution</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Mathematics</subject><subject>Methods</subject><subject>Optical imaging</subject><subject>Optimization</subject><subject>Optimization and Control</subject><subject>Optimization techniques</subject><subject>Outliers (statistics)</subject><subject>Physical properties</subject><subject>Physics</subject><subject>Random noise</subject><subject>Representations</subject><subject>satellite image time-series</subject><subject>Satellite imagery</subject><subject>Satellites</subject><subject>segmentation</subject><subject>Signal and Image Processing</subject><subject>slow-moving landslides</subject><subject>sparsity</subject><subject>Terrain factors</subject><subject>Time series</subject><subject>Time series analysis</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>eNo9kFFPwjAUhRujiYj-AONLE598GLalW7tHggokEBKHz7Xb7qBkrNgWDP56hxCfTnLPd05uDkL3lPQoJenzYvSe9RihssekoHFMLlCnFRmRhPNL1CE0TSImU3aNbrxfE0J5TEUHfWZb7bwJBzzfBrMxPzoY2-AZhJUtcWUdzmr7Hc3s3jRLPNVN6WtTgscvEKD4Y02DMx2grk0APNnoJeCF2UCUgTPgb9FVpWsPd2ftoo-318VwHE3no8lwMI2KPhMhyqEstASdgKjyoujHIHMaUykTInJBy0SkQvJYJJWEtCiJbu2CMM4YT_K4LPtd9HTqXelabZ3ZaHdQVhs1HkzV8UYYSRhP-Z627OOJ3Tr7tQMf1NruXNO-pxgjRDIuZdpS9EQVznrvoPqvpUQdR1fH0dVxdHUevc08nDIGAP55yVPWb91fa7R9hg</recordid><startdate>20190401</startdate><enddate>20190401</enddate><creator>Pham, Mai Quyen</creator><creator>Lacroix, Pascal</creator><creator>Doin, Marie Pierre</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</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><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-1482-0600</orcidid><orcidid>https://orcid.org/0000-0002-9546-4005</orcidid><orcidid>https://orcid.org/0000-0002-9974-2455</orcidid></search><sort><creationdate>20190401</creationdate><title>Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series</title><author>Pham, Mai Quyen ; Lacroix, Pascal ; Doin, Marie Pierre</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c327t-bedca8ea6e7fbcc35e8b15188607b71d679784576f8e9cd0a8b1c0242246b5dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science</topic><topic>Correlation</topic><topic>Data</topic><topic>Data processing</topic><topic>Detection</topic><topic>Errors</topic><topic>Field investigations</topic><topic>Field tests</topic><topic>Geophysics</topic><topic>Image detection</topic><topic>Image resolution</topic><topic>Landslides</topic><topic>Landslides & mudslides</topic><topic>Mathematics</topic><topic>Methods</topic><topic>Optical imaging</topic><topic>Optimization</topic><topic>Optimization and Control</topic><topic>Optimization techniques</topic><topic>Outliers (statistics)</topic><topic>Physical properties</topic><topic>Physics</topic><topic>Random noise</topic><topic>Representations</topic><topic>satellite image time-series</topic><topic>Satellite imagery</topic><topic>Satellites</topic><topic>segmentation</topic><topic>Signal and Image Processing</topic><topic>slow-moving landslides</topic><topic>sparsity</topic><topic>Terrain factors</topic><topic>Time series</topic><topic>Time series analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pham, Mai Quyen</creatorcontrib><creatorcontrib>Lacroix, Pascal</creatorcontrib><creatorcontrib>Doin, Marie Pierre</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><collection>Hyper Article en Ligne (HAL)</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pham, Mai Quyen</au><au>Lacroix, Pascal</au><au>Doin, Marie Pierre</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2019-04-01</date><risdate>2019</risdate><volume>57</volume><issue>4</issue><spage>2133</spage><epage>2144</epage><pages>2133-2144</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract><![CDATA[This paper presents a new method based on recent optimization technique to detect slow-moving landslides (<150m/year) in time series of displacement field generated by satellite images. Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed <inline-formula> <tex-math notation="LaTeX">\ell _{1,2} </tex-math></inline-formula>-norm is the most suitable norm for this detection problem, compared to pure <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>-norm or <inline-formula> <tex-math notation="LaTeX">\ell _{2} </tex-math></inline-formula>-norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2018.2871550</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-1482-0600</orcidid><orcidid>https://orcid.org/0000-0002-9546-4005</orcidid><orcidid>https://orcid.org/0000-0002-9974-2455</orcidid></addata></record> |
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| ispartof | IEEE transactions on geoscience and remote sensing, 2019-04, Vol.57 (4), p.2133-2144 |
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| subjects | Computer Science Correlation Data Data processing Detection Errors Field investigations Field tests Geophysics Image detection Image resolution Landslides Landslides & mudslides Mathematics Methods Optical imaging Optimization Optimization and Control Optimization techniques Outliers (statistics) Physical properties Physics Random noise Representations satellite image time-series Satellite imagery Satellites segmentation Signal and Image Processing slow-moving landslides sparsity Terrain factors Time series Time series analysis |
| title | Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series |
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