{"id":"https://openalex.org/W4386598479","doi":"https://doi.org/10.1109/icip49359.2023.10222789","title":"DSG-PL: ROI Extraction Based on Dual Saliency Guided Progressive Learning for Weakly Labeled Remote Sensing Images","display_name":"DSG-PL: ROI Extraction Based on Dual Saliency Guided Progressive Learning for Weakly Labeled Remote Sensing Images","publication_year":2023,"publication_date":"2023-09-11","ids":{"openalex":"https://openalex.org/W4386598479","doi":"https://doi.org/10.1109/icip49359.2023.10222789"},"language":"en","primary_location":{"id":"doi:10.1109/icip49359.2023.10222789","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","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/A5100387415","display_name":"Yanan Liu","orcid":"https://orcid.org/0009-0001-7490-6177"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanan Liu","raw_affiliation_strings":["Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087779583","display_name":"Libao Zhang","orcid":"https://orcid.org/0000-0002-0888-2330"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libao Zhang","raw_affiliation_strings":["Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100387415"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16442435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2840","last_page":"2844"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994999766349792,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994999766349792,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7939222455024719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7240729928016663},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7189273238182068},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6484293937683105},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5962028503417969},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.49427103996276855},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4586167335510254},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4473973512649536},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.4467320740222931},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4406406283378601},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41622394323349},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41420772671699524},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4049786925315857},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38341718912124634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3460335433483124},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1176486611366272}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939222455024719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7240729928016663},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7189273238182068},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6484293937683105},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5962028503417969},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.49427103996276855},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4586167335510254},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4473973512649536},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.4467320740222931},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4406406283378601},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41622394323349},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41420772671699524},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4049786925315857},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38341718912124634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3460335433483124},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1176486611366272},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip49359.2023.10222789","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip49359.2023.10222789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G1023919524","display_name":null,"funder_award_id":", Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3872703625","display_name":null,"funder_award_id":"41771407","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5547827018","display_name":null,"funder_award_id":"61571050","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7728640597","display_name":null,"funder_award_id":"62271060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1910204705","https://openalex.org/W1963816830","https://openalex.org/W2206278467","https://openalex.org/W2221898772","https://openalex.org/W2295107390","https://openalex.org/W2494236530","https://openalex.org/W2585521554","https://openalex.org/W2600144439","https://openalex.org/W2765569127","https://openalex.org/W2791575150","https://openalex.org/W2798715809","https://openalex.org/W2961348656","https://openalex.org/W2962758679","https://openalex.org/W2963685207","https://openalex.org/W2964274719","https://openalex.org/W3034873438","https://openalex.org/W3034930876","https://openalex.org/W3118335270","https://openalex.org/W3125080173","https://openalex.org/W3158157755","https://openalex.org/W3196990496","https://openalex.org/W4226396876","https://openalex.org/W4291652920","https://openalex.org/W4312566218"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2571255492","https://openalex.org/W4239293476"],"abstract_inverted_index":{"Region":[0],"of":[1,18,63,87,149,171],"Interest":[2],"(ROI)":[3],"extraction":[4],"from":[5,65],"weakly":[6,24],"annotated":[7],"remote":[8],"sensing":[9],"images":[10],"(RSIs)":[11],"can":[12],"save":[13],"the":[14,61,66,70,85,109,121,147,157,169,172],"huge":[15],"labor":[16],"cost":[17],"labelling":[19],"accurate":[20],"pixel-level":[21],"annotations.":[22],"However,":[23],"supervised":[25,40],"approaches":[26],"with":[27,38,151,160],"sparseness":[28],"and":[29,95,165],"incompleteness":[30],"inevitably":[31],"result":[32],"in":[33,91,119],"a":[34,46,76,88,129,139],"performance":[35],"gap":[36],"compared":[37],"fully":[39],"counterparts.":[41],"To":[42,74],"tackle":[43],"this":[44],"issue,":[45],"dual":[47,77],"saliency":[48,78],"guided":[49],"progressive":[50],"learning":[51],"(DSG-PL)":[52],"framework":[53],"is":[54,81,117,143],"developed,":[55],"which":[56,120],"focuses":[57],"on":[58],"progressively":[59],"enhancing":[60],"quality":[62],"supervision":[64],"image":[67],"level":[68],"to":[69,83,106,126,145],"precise":[71],"pixel":[72],"level.":[73],"begin,":[75],"constraint":[79],"mechanism":[80],"created":[82],"guide":[84],"training":[86,150],"classification":[89],"network":[90],"both":[92],"an":[93,112],"explicit":[94],"implicit":[96],"manner":[97],"for":[98],"generating":[99],"integral":[100],"pixel-wise":[101,158],"pseudo":[102],"labels":[103,123],"(PLs).":[104],"Then,":[105],"gradually":[107],"refine":[108],"initial":[110],"PLs,":[111],"adaptive":[113],"label":[114],"self-correction":[115],"module":[116],"presented,":[118],"updated":[122],"are":[124],"used":[125],"iteratively":[127],"train":[128],"context-enhanced":[130],"segmentation":[131],"network,":[132],"therefore":[133],"constantly":[134],"boosting":[135],"model":[136],"performance.":[137],"Finally,":[138],"confidence-aware":[140],"denoising":[141],"loss":[142,159],"intended":[144],"alleviate":[146],"impacts":[148],"noisy":[152],"PLs":[153],"by":[154],"adaptively":[155],"reweighting":[156],"confidence":[161],"scores.":[162],"Comprehensive":[163],"evaluations":[164],"ablation":[166],"studies":[167],"verify":[168],"superiority":[170],"proposed":[173],"DSG-PL.":[174]},"counts_by_year":[],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}
