{"id":"https://openalex.org/W2154865385","doi":"https://doi.org/10.1109/igarss.2007.4423786","title":"Robust measurement of glacier surface motion from multiscale speckle tracking using local constraints","display_name":"Robust measurement of glacier surface motion from multiscale speckle tracking using local constraints","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2154865385","doi":"https://doi.org/10.1109/igarss.2007.4423786","mag":"2154865385"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2007.4423786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003388985","display_name":"Esra Erten","orcid":"https://orcid.org/0000-0002-4208-7170"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Esra Erten","raw_affiliation_strings":["Computer Vision and Remote Sensing, Berlin University of Technology, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Remote Sensing, Berlin University of Technology, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110492846","display_name":"Andreas Reigber","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andreas Reigber","raw_affiliation_strings":["Computer Vision and Remote Sensing, Berlin University of Technology, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Remote Sensing, Berlin University of Technology, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065265845","display_name":"Marc Jaeger","orcid":"https://orcid.org/0000-0001-9685-2977"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marc Jaeger","raw_affiliation_strings":["Computer Vision and Remote Sensing, Berlin University of Technology, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Remote Sensing, Berlin University of Technology, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081055268","display_name":"Olaf Hellwich","orcid":"https://orcid.org/0000-0002-2871-9266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olaf Hellwich","raw_affiliation_strings":["Computer Vision and Remote Sensing, Berlin University of Technology, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision and Remote Sensing, Berlin University of Technology, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11440619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4237","last_page":"4240"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10644","display_name":"Cryospheric studies and observations","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10644","display_name":"Cryospheric studies and observations","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11333","display_name":"Climate change and permafrost","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5113574862480164},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.5093961358070374},{"id":"https://openalex.org/keywords/vector-field","display_name":"Vector field","score":0.46814945340156555},{"id":"https://openalex.org/keywords/motion-field","display_name":"Motion field","score":0.44642001390457153},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.44351616501808167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4342010021209717},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.4242039620876312},{"id":"https://openalex.org/keywords/geodesy","display_name":"Geodesy","score":0.41676652431488037},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41476017236709595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40814244747161865},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4033474326133728},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30066296458244324},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.13491535186767578}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5113574862480164},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.5093961358070374},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.46814945340156555},{"id":"https://openalex.org/C124774092","wikidata":"https://www.wikidata.org/wiki/Q6917782","display_name":"Motion field","level":3,"score":0.44642001390457153},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.44351616501808167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4342010021209717},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.4242039620876312},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.41676652431488037},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41476017236709595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40814244747161865},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4033474326133728},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30066296458244324},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.13491535186767578}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2007.4423786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.953.7837","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.953.7837","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv.tu-berlin.de/fileadmin/fg140/Robust_Measurement_of.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2001002651","https://openalex.org/W2111471486","https://openalex.org/W2123896593","https://openalex.org/W4285719527","https://openalex.org/W4300311439"],"related_works":["https://openalex.org/W1501297619","https://openalex.org/W2053820737","https://openalex.org/W2120943489","https://openalex.org/W1484667368","https://openalex.org/W1988727984","https://openalex.org/W2130582613","https://openalex.org/W2020088946","https://openalex.org/W2026674073","https://openalex.org/W2788703256","https://openalex.org/W2157486817"],"abstract_inverted_index":{"A":[0],"grown":[1],"importance":[2],"in":[3,58,124,228,237,251,257],"long-term":[4],"operational":[5],"glacier":[6,16,36,52,77],"monitoring":[7],"has":[8,55],"emerged,":[9],"mainly":[10,24],"due":[11],"to":[12,18,22,83,97,180,207,215],"the":[13,33,46,73,76,100,143,163,182,202,221,223,245,248,252],"connection":[14],"of":[15,27,35,49,88,110,152,239,247,254,259],"recession":[17],"climate":[19],"changes.":[20],"Up":[21],"now,":[23],"two":[25],"types":[26],"methods":[28],"have":[29],"been":[30,56],"used":[31,179],"for":[32,51,141,186],"estimation":[34,54],"flow":[37,204],"velocities:":[38],"Image":[39],"matching":[40],"and":[41,126,146,173,219,231],"differential":[42],"interferometry":[43],"(DInSAR).":[44],"Although":[45],"principal":[47],"potential":[48],"DInSAR":[50,89,125],"velocity":[53,78,144,211],"shown":[57],"several":[59],"case":[60,238,258],"studies,":[61],"its":[62],"successful":[63],"application":[64],"is":[65,79,160,178],"often":[66,80],"limited":[67,118],"by":[68,72,86,119,192],"phase":[69,120],"noise,":[70,255],"described":[71],"coherence.":[74],"Additionally,":[75],"too":[81,95],"large":[82,101],"be":[84,94,129],"analysed":[85],"means":[87],"since":[90],"this":[91,136,190],"method":[92],"can":[93,127],"sensitive":[96],"correctly":[98],"track":[99],"displacements":[102],"occurring":[103],"during":[104],"a":[105,132,138,150,193,208],"typical":[106],"data":[107],"acquisition":[108],"interval":[109],"one":[111],"month.":[112],"SAR":[113,153,260],"amplitude":[114,154],"images":[115,155],"are":[116,156,199,226],"not":[117],"stability":[121],"problems":[122],"like":[123],"reliably":[128],"acquired":[130],"on":[131,162],"regular":[133],"basis.":[134],"In":[135,213],"work,":[137],"novel":[139],"algorithm":[140,159],"computing":[142],"field":[145],"motion":[147,184,218,249],"parameters":[148],"from":[149],"sequence":[151],"presented.":[157],"The":[158,176],"based":[161],"vector":[164],"relaxation":[165,194],"combined":[166],"with":[167],"standardized":[168],"cross-":[169],"covariance":[170],"matrix":[171],"information":[172],"cross-correlation":[174,177],"techniques.":[175],"indicate":[181],"candidate":[183],"vectors":[185],"each":[187],"pixel.":[188],"After":[189],"step,":[191],"operation":[195],"local":[196],"smoothness":[197],"constraints":[198],"introduced":[200],"into":[201],"estimated":[203],"pattern,":[205],"leading":[206],"more":[209],"homogeneous":[210],"estimation.":[212],"order":[214],"handle":[216],"fast":[217],"reduce":[220],"mismatches,":[222],"mentioned":[224],"algorithms":[225],"applied":[227],"different":[229],"scales":[230],"linked":[232],"using":[233],"anisotropic":[234],"diffusion":[235],"equation":[236],"multiscale":[240],"cross-correlation.":[241],"This":[242],"significantly":[243],"improves":[244],"reliability":[246],"detection":[250],"presence":[253],"inherent":[256],"data.":[261]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
