{"id":"https://openalex.org/W7160322938","doi":"https://doi.org/10.48550/arxiv.2605.02471","title":"Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation","display_name":"Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160322938","doi":"https://doi.org/10.48550/arxiv.2605.02471"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02471","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02471","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02471","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088225634","display_name":"Mete Ahishali","orcid":"https://orcid.org/0000-0003-0937-5194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahishali, Mete","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060351601","display_name":"Anis Ur Rahman","orcid":"https://orcid.org/0000-0002-8306-475X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Anis Ur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075049432","display_name":"Einari Heinaro","orcid":"https://orcid.org/0000-0003-2606-6131"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heinaro, Einari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057088639","display_name":"Aysen Degerli","orcid":"https://orcid.org/0000-0002-9478-033X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Degerli, Aysen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135336218","display_name":"Samuli Junttila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junttila, Samuli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.3846000134944916,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.3846000134944916,"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":0.15410000085830688,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.15219999849796295,"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/multispectral-image","display_name":"Multispectral image","score":0.7562000155448914},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5860000252723694},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.5690000057220459},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.507099986076355},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.4242999851703644},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4153999984264374},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7562000155448914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7470999956130981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7383000254631042},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5690000057220459},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.507099986076355},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.4242999851703644},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4153999984264374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3077000081539154},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02471","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02471","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02471","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02471","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.592931866645813}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mapping":[0],"standing":[1,147],"dead":[2,26,148],"trees":[3],"is":[4,130,167,177,185],"crucial":[5],"for":[6,25,140],"acquiring":[7],"information":[8],"on":[9,15,75,179,187],"the":[10,38,62,90,94,124,131,144,164,174,188,194],"effects":[11],"of":[12,40,126,146,156],"climate":[13],"change":[14],"forests":[16],"and":[17,37,110,137,158],"forest":[18],"biodiversity.":[19],"However,":[20],"leveraging":[21],"high-quality":[22],"aerial":[23,195],"imagery":[24],"tree":[27,149],"segmentation":[28,154,175],"poses":[29],"challenges":[30],"due":[31],"to":[32,60,66,134],"limitations":[33],"in":[34,116,143,160],"sensor":[35],"availability":[36],"scarcity":[39],"annotated":[41,200],"data.":[42,190],"In":[43],"this":[44,129],"study,":[45],"we":[46],"propose":[47],"a":[48,99],"generic":[49,138],"blind":[50],"super-resolution":[51,139],"framework":[52],"that":[53,113],"incorporates":[54],"Attention-Guided":[55],"Domain":[56],"Adaptation":[57],"Networks":[58],"(ADA-Nets)":[59],"learn":[61],"mapping":[63],"from":[64],"low-resolution":[65,82,117,181],"high-resolution":[67,91,172,189],"multispectral":[68,141,196],"image":[69,104],"domains.":[70],"Our":[71],"approach":[72],"operates":[73],"solely":[74],"unpaired":[76],"samples,":[77],"mimicking":[78],"real-world":[79,136],"conditions,":[80],"i.e.,":[81],"images":[83,118],"are":[84],"not":[85],"synthetically":[86],"obtained":[87,168],"by":[88,120],"downsampling":[89],"images.":[92],"Moreover,":[93],"proposed":[95],"method":[96],"serves":[97],"as":[98],"general-purpose":[100],"restorer":[101],"addressing":[102],"several":[103],"degradation":[105],"types,":[106],"including":[107],"saturation,":[108],"noise,":[109],"low":[111],"contrast":[112],"typically":[114],"occur":[115],"acquired":[119],"low-end":[121],"sensors.":[122],"To":[123],"best":[125],"our":[127],"knowledge,":[128],"first":[132,165],"study":[133],"perform":[135],"data":[142],"scope":[145],"segmentation.":[150],"Experimental":[151],"evaluations":[152],"demonstrate":[153],"performances":[155],"54%":[157],"64%":[159],"Dice":[161],"scores.":[162],"Notably,":[163],"result":[166],"without":[169],"using":[170],"any":[171],"annotations;":[173],"network":[176],"trained":[178],"super-resolved":[180],"images,":[182],"while":[183],"evaluation":[184],"performed":[186],"We":[191],"publicly":[192],"share":[193],"dataset":[197],"with":[198],"manually":[199],"labels":[201],"at":[202],"https://www.kaggle.com/datasets/meteahishali/aerial-imagery-for-dead-tree-segmentation-poland.":[203]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}
