{"id":"https://openalex.org/W3160709777","doi":"https://doi.org/10.1109/icassp39728.2021.9413823","title":"Image Generation Based on Texture Guided VAE-AGAN for Regions of Interest Detection in Remote Sensing Images","display_name":"Image Generation Based on Texture Guided VAE-AGAN for Regions of Interest Detection in Remote Sensing Images","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3160709777","doi":"https://doi.org/10.1109/icassp39728.2021.9413823","mag":"3160709777"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9413823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/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":true,"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"]}]},{"author_position":"last","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":false,"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"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5087779583"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.374,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63015106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2310","last_page":"2314"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9915000200271606,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9897000193595886,"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/autoencoder","display_name":"Autoencoder","score":0.8828275203704834},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7956244945526123},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.784054160118103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7078996896743774},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.6417839527130127},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.6075235605239868},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5498268604278564},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48793455958366394},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48149335384368896},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47726649045944214},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44662389159202576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4461617171764374},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.42942821979522705},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4232056736946106},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41708946228027344},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.4144514203071594},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.34551405906677246},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.20662832260131836},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.15605339407920837},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10989660024642944},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06202876567840576}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8828275203704834},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7956244945526123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784054160118103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7078996896743774},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.6417839527130127},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.6075235605239868},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5498268604278564},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48793455958366394},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48149335384368896},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47726649045944214},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44662389159202576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4461617171764374},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.42942821979522705},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4232056736946106},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41708946228027344},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.4144514203071594},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34551405906677246},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20662832260131836},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.15605339407920837},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10989660024642944},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06202876567840576},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9413823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321432","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W1963816830","https://openalex.org/W1997137149","https://openalex.org/W2099471712","https://openalex.org/W2517325737","https://openalex.org/W2534181202","https://openalex.org/W2555089714","https://openalex.org/W2569272946","https://openalex.org/W2593560791","https://openalex.org/W2610166850","https://openalex.org/W2726922549","https://openalex.org/W2739748921","https://openalex.org/W2790482089","https://openalex.org/W2791575150","https://openalex.org/W2908685061","https://openalex.org/W2914311543","https://openalex.org/W2945231726","https://openalex.org/W2963684088","https://openalex.org/W2963814095","https://openalex.org/W2963981733","https://openalex.org/W2964167449","https://openalex.org/W2967085153","https://openalex.org/W3025579971","https://openalex.org/W3101896960","https://openalex.org/W3102864715","https://openalex.org/W3103912303","https://openalex.org/W4301206121","https://openalex.org/W4320013936","https://openalex.org/W6640963894","https://openalex.org/W6685352114","https://openalex.org/W6687506355","https://openalex.org/W6741832134","https://openalex.org/W6749814210","https://openalex.org/W6765779288"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W4380714744","https://openalex.org/W2387995142","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2964074194","https://openalex.org/W2057775761","https://openalex.org/W2366944513"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"has":[2],"shown":[3],"great":[4],"strength":[5],"in":[6,101],"regions":[7,116],"of":[8,20,25,34,74,111,117,149],"interest":[9],"(ROIs)":[10],"detection":[11,135],"for":[12,18,65],"remote":[13],"sensing":[14],"images":[15],"(RSIs).":[16],"However,":[17],"most":[19],"RSIs,":[21,75],"the":[22,32,35,62,95,102,108,123,131,141,147],"unbalanced":[23],"distribution":[24],"positive":[26],"and":[27,89,97,114],"negative":[28],"samples":[29],"greatly":[30],"limits":[31],"performance":[33,148],"deep":[36],"learning-based":[37],"methods.":[38,158],"To":[39],"cope":[40],"with":[41],"this":[42],"issue,":[43],"we":[44,76,93,121],"propose":[45,77],"a":[46,78],"novel":[47],"method":[48],"based":[49],"on":[50],"texture":[51,72,79,84],"guided":[52],"variational":[53],"autoencoder-attention":[54],"wise":[55],"generative":[56],"adversarial":[57],"network":[58],"(VAE-AGAN)":[59],"to":[60,69,82,105,130],"augment":[61],"training":[63],"data":[64],"ROI":[66,134,150],"detection.":[67],"First,":[68],"generate":[70],"realistic":[71],"details":[73],"guidance":[80],"block":[81],"embed":[83],"prior":[85],"information":[86],"into":[87],"encoder":[88],"decoder":[90],"networks.":[91],"Second,":[92],"introduce":[94],"channel":[96],"spatial-wise":[98],"attention":[99],"layers":[100],"discriminator":[103],"construct":[104],"adaptively":[106],"recalibrate":[107],"varying":[109],"importance":[110],"different":[112],"channels":[113],"spatial":[115],"input":[118],"RSIs.":[119],"Finally,":[120],"apply":[122],"RSI":[124],"dataset":[125],"balanced":[126],"by":[127],"our":[128],"proposal":[129,142],"weakly":[132],"supervised":[133],"method.":[136],"Experimental":[137],"results":[138],"demonstrate":[139],"that":[140],"can":[143],"not":[144],"only":[145],"improve":[146],"detection,":[151],"but":[152],"also":[153],"outperform":[154],"other":[155],"competing":[156],"augmentation":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
