{"id":"https://openalex.org/W3207667039","doi":"https://doi.org/10.1109/igarss47720.2021.9554277","title":"DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification","display_name":"DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207667039","doi":"https://doi.org/10.1109/igarss47720.2021.9554277","mag":"3207667039"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554277","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.10411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102791407","display_name":"Daniel F. S. Santos","orcid":"https://orcid.org/0000-0001-5124-3713"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Daniel F.S. Santos","raw_affiliation_strings":["S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042862894","display_name":"Rafael G. Pires","orcid":"https://orcid.org/0000-0001-9597-055X"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael G. Pires","raw_affiliation_strings":["S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041190229","display_name":"Leandro A. Passos","orcid":"https://orcid.org/0000-0003-3529-3109"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leandro A. Passos","raw_affiliation_strings":["S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003275797","display_name":"Jo\u00e3o Paulo Papa","orcid":"https://orcid.org/0000-0002-6494-7514"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao P. Papa","raw_affiliation_strings":["S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"S\u00e3o Paulo State University - UNESP, Bauru, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102791407"],"corresponding_institution_ids":["https://openalex.org/I879563668"],"apc_list":null,"apc_paid":null,"fwci":0.3775,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.65732682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2843","last_page":"2846"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7749215364456177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6991147994995117},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6360346078872681},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5939899682998657},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5777219533920288},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5345332622528076},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4910280704498291},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.44169795513153076},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.4254133701324463},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4175177812576294},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.4158340096473694},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3840915262699127},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1026245653629303},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1006961464881897}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7749215364456177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6991147994995117},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6360346078872681},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5939899682998657},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5777219533920288},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5345332622528076},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4910280704498291},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.44169795513153076},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.4254133701324463},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4175177812576294},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.4158340096473694},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3840915262699127},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1026245653629303},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1006961464881897},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554277","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554277","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2212.10411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.10411","pdf_url":"https://arxiv.org/pdf/2212.10411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.10411","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.10411","pdf_url":"https://arxiv.org/pdf/2212.10411","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2070100647","display_name":null,"funder_award_id":"#2013/07375-0,#2014/12236-1,#2019/07665-4,#2020/12101-0","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3454416795","display_name":null,"funder_award_id":"#307066/2017-7,#427968/2018-6","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1980038761","https://openalex.org/W2098676252","https://openalex.org/W2173520492","https://openalex.org/W2190186811","https://openalex.org/W2347115704","https://openalex.org/W2515866431","https://openalex.org/W2592962403","https://openalex.org/W2620429297","https://openalex.org/W2621526417","https://openalex.org/W2767089292","https://openalex.org/W2771305881","https://openalex.org/W2787070260","https://openalex.org/W2789619030","https://openalex.org/W2799466885","https://openalex.org/W2807872667","https://openalex.org/W2919352650","https://openalex.org/W2963684088","https://openalex.org/W2964013315","https://openalex.org/W3103856189","https://openalex.org/W3104724358","https://openalex.org/W3105577662","https://openalex.org/W6685352114","https://openalex.org/W6687157873"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2362114017","https://openalex.org/W3147024994","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W1978302214","https://openalex.org/W2021817983","https://openalex.org/W3008559849","https://openalex.org/W2371177901"],"abstract_inverted_index":{"Research":[0],"on":[1],"remote":[2,30,83,99],"sensing":[3,31,84,100],"image":[4,50,85,96,101],"classification":[5,51],"significantly":[6],"impacts":[7],"essential":[8],"human":[9],"routine":[10],"tasks":[11],"such":[12],"as":[13],"urban":[14],"planning":[15],"and":[16,24,60,72],"agriculture.":[17],"Nowadays,":[18],"the":[19,25,54,61,91],"rapid":[20],"advance":[21],"in":[22],"technology":[23],"availability":[26],"of":[27,93],"many":[28],"high-quality":[29],"images":[32],"create":[33],"a":[34],"demand":[35],"for":[36,49,98],"reliable":[37],"automation":[38],"methods.":[39],"The":[40],"current":[41],"paper":[42],"proposes":[43],"two":[44],"novel":[45],"deep":[46,95],"learning-based":[47],"architectures":[48],"purposes,":[52],"i.e.,":[53],"Discriminant":[55,62],"Deep":[56,63,69],"Image":[57,64,70],"Prior":[58,65,71],"Network":[59],"Network+,":[66],"which":[67],"combine":[68],"Triplet":[73],"Networks":[74],"learning":[75],"strategies.":[76],"Experiments":[77],"conducted":[78],"over":[79],"three":[80],"well-known":[81],"public":[82],"datasets":[86],"achieved":[87],"state-of-the-art":[88],"results,":[89],"evidencing":[90],"effectiveness":[92],"using":[94],"priors":[97],"classification.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2021-10-25T00:00:00"}