{"id":"https://openalex.org/W2774810635","doi":"https://doi.org/10.1109/igarss.2017.8127432","title":"Classification of multitemporal SAR images using convolutional neural networks and Markov random fields","display_name":"Classification of multitemporal SAR images using convolutional neural networks and Markov random fields","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2774810635","doi":"https://doi.org/10.1109/igarss.2017.8127432","mag":"2774810635"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ris.utwente.nl/ws/files/503447231/Classification_of_multitemporal_SAR_images_using_convolutional_neural_networks_and_Markov_random_fields.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026429194","display_name":"Carolyne Danilla","orcid":null},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Carolyne Danilla","raw_affiliation_strings":["Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029035818","display_name":"Claudio Persello","orcid":"https://orcid.org/0000-0003-3742-5398"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Claudio Persello","raw_affiliation_strings":["Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010072631","display_name":"Valentyn Tolpekin","orcid":"https://orcid.org/0000-0003-4517-113X"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Valentyn Tolpekin","raw_affiliation_strings":["Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006207870","display_name":"John Ray Bergado","orcid":"https://orcid.org/0000-0001-7843-5512"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"John Ray Bergado","raw_affiliation_strings":["Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Earth Observation Science, University of Twente, Enschede, The Netherlands","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":null,"apc_paid":null,"fwci":4.562,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.94918226,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"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":0.7110113501548767},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7076292634010315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7050682306289673},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.700991153717041},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.6835224032402039},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6724205017089844},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.667330801486969},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6075704097747803},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.6071571707725525},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.523862361907959},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.49121445417404175},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.4757392406463623},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.34210777282714844},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3343045711517334},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.321585476398468},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.25548750162124634},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1726984977722168}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7110113501548767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7076292634010315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7050682306289673},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.700991153717041},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.6835224032402039},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6724205017089844},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.667330801486969},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6075704097747803},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.6071571707725525},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.523862361907959},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49121445417404175},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.4757392406463623},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.34210777282714844},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3343045711517334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.321585476398468},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.25548750162124634},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1726984977722168},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss.2017.8127432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:publications/79d6c62f-8961-410f-a8e3-b0e5ef0e66ef","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/79d6c62f-8961-410f-a8e3-b0e5ef0e66ef","pdf_url":"https://ris.utwente.nl/ws/files/503447231/Classification_of_multitemporal_SAR_images_using_convolutional_neural_networks_and_Markov_random_fields.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Danilla, C, Persello, C, Tolpekin, V & Bergado, J R 2017, Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) : 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, pp. 2231-2234, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, Texas, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8127432","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/79d6c62f-8961-410f-a8e3-b0e5ef0e66ef","is_oa":true,"landing_page_url":"https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2017/chap/persello_cla.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Danilla, C, Persello, C, Tolpekin, V & Bergado, J R 2017, Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) : 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, pp. 2231-2234, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, Texas, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8127432","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:publications/79d6c62f-8961-410f-a8e3-b0e5ef0e66ef","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/79d6c62f-8961-410f-a8e3-b0e5ef0e66ef","pdf_url":"https://ris.utwente.nl/ws/files/503447231/Classification_of_multitemporal_SAR_images_using_convolutional_neural_networks_and_Markov_random_fields.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Danilla, C, Persello, C, Tolpekin, V & Bergado, J R 2017, Classification of multitemporal SAR images using convolutional neural networks and Markov random fields. in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) : 23-28 Jly 2017, Fort Worth Texas, USA. IEEE, pp. 2231-2234, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, Texas, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8127432","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2774810635.pdf","grobid_xml":"https://content.openalex.org/works/W2774810635.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1602107991","https://openalex.org/W2095705004","https://openalex.org/W2444349171","https://openalex.org/W2548960422","https://openalex.org/W3048333588","https://openalex.org/W4302190632","https://openalex.org/W6674330103","https://openalex.org/W6718687703"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2113052720","https://openalex.org/W2799624451","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2055824452","https://openalex.org/W2121688719","https://openalex.org/W2016481886"],"abstract_inverted_index":{"Classification":[0],"of":[1,12,15,35,46,84,104,108,124],"Synthetic":[2],"Aperture":[3],"Radar":[4],"(SAR)":[5],"images":[6,19,110],"is":[7],"a":[8,21,25,44,105],"complex":[9],"task":[10],"because":[11],"the":[13,33,82,87,97,102,122,125],"presence":[14],"speckle,":[16],"which":[17,40,128],"affects":[18],"in":[20,115],"way":[22],"similar":[23],"to":[24,49,60,79,91,101],"strong":[26],"noise.":[27],"In":[28],"this":[29],"study,":[30],"we":[31,67],"investigate":[32],"use":[34],"Convolutional":[36],"Neural":[37],"Networks":[38],"(CNNs)":[39],"can":[41],"effectively":[42],"learn":[43],"bank":[45],"spatial":[47],"filters":[48],"simultaneously":[50],"1)":[51],"reduce":[52,81],"speckle":[53,85],"noise,":[54],"and":[55,63,90],"2)":[56],"extract":[57],"spatial-contextual":[58],"features":[59],"characterize":[61],"texture":[62],"scattering":[64],"mechanism.":[65],"Moreover,":[66],"combine":[68],"CNN":[69],"with":[70],"Markov":[71],"Random":[72],"Fields":[73],"(MRFs)":[74],"for":[75,111],"post-classification":[76],"label":[77],"smoothing":[78],"further":[80],"effect":[83],"on":[86],"land-cover":[88],"map":[89],"improve":[92],"classification":[93,99],"accuracy.":[94],"We":[95],"applied":[96],"proposed":[98],"system":[100],"analysis":[103],"multitemporal":[106],"series":[107],"Sentinel-1":[109],"mapping":[112],"agricultural":[113],"fields":[114],"Flevoland,":[116],"The":[117],"Netherlands.":[118],"Experimental":[119],"results":[120],"confirm":[121],"effectiveness":[123],"investigated":[126],"approach,":[127],"outperforms":[129],"standard":[130],"methods.":[131]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-08-23T07:36:19.812096","created_date":"2025-10-10T00:00:00"}
