{"id":"https://openalex.org/W7128714918","doi":"https://doi.org/10.48550/arxiv.2602.10137","title":"Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation","display_name":"Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation","publication_year":2026,"publication_date":"2026-02-08","ids":{"openalex":"https://openalex.org/W7128714918","doi":"https://doi.org/10.48550/arxiv.2602.10137"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10137","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000019015","display_name":"Leo Ramos","orcid":"https://orcid.org/0000-0001-7107-7943"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramos, Leo Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002373986","display_name":"\u00c1ngel D. Sappa","orcid":"https://orcid.org/0000-0003-2468-0031"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sappa, Angel D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12990462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.8463000059127808,"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":0.8463000059127808,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.07000000029802322,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.018200000748038292,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7332000136375427},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.63919997215271},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6069999933242798},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.525600016117096},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.520799994468689},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.498199999332428},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.445499986410141},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.367000013589859}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7332000136375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254999876022339},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6845999956130981},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.63919997215271},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6069999933242798},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4320000112056732},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.30630001425743103},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10137","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.10137","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10137","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.10137","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"work":[1],"proposes":[2],"MeCSAFNet,":[3],"a":[4,83,95],"multi-branch":[5],"encoder-decoder":[6],"architecture":[7],"for":[8],"land":[9],"cover":[10],"segmentation":[11],"in":[12,138,163,195],"multispectral":[13],"imagery.":[14],"The":[15,53,73,165],"model":[16,74,166],"separately":[17],"processes":[18],"visible":[19],"and":[20,62,70,89,101,109,133,158,188],"non-visible":[21],"channels":[22],"through":[23],"dual":[24],"ConvNeXt":[25],"encoders,":[26],"followed":[27],"by":[28,123,127,131,136,148,152,156,161],"individual":[29],"decoders":[30],"that":[31],"reconstruct":[32],"spatial":[33,47],"information.":[34],"A":[35],"dedicated":[36],"fusion":[37,55],"decoder":[38],"integrates":[39],"intermediate":[40],"features":[41],"at":[42],"multiple":[43],"scales,":[44],"combining":[45,87],"fine":[46],"cues":[48],"with":[49,59,184],"high-level":[50],"spectral":[51,80],"representations.":[52],"feature":[54],"is":[56,75],"further":[57],"enhanced":[58],"CBAM":[60],"attention,":[61],"the":[63,106],"ASAU":[64],"activation":[65],"function":[66],"contributes":[67],"to":[68,77],"stable":[69],"efficient":[71],"optimization.":[72],"designed":[76],"process":[78],"different":[79],"configurations,":[81],"including":[82],"4-channel":[84],"(4c)":[85,122,130,143,147,155],"input":[86,98],"RGB":[88],"NIR":[90],"bands,":[91],"as":[92,94],"well":[93],"6-channel":[96],"(6c)":[97,119,126,135,151,160],"incorporating":[99],"NDVI":[100],"NDWI":[102],"indices.":[103],"Experiments":[104],"on":[105],"Five-Billion-Pixels":[107],"(FBP)":[108],"Potsdam":[110],"datasets":[111],"demonstrate":[112],"significant":[113],"performance":[114,183],"gains.":[115],"On":[116,140],"FBP,":[117],"MeCSAFNet-base":[118],"surpasses":[120],"U-Net":[121,125],"+19.21%,":[124],"+14.72%,":[128],"SegFormer":[129,134,154,159],"+19.62%,":[132],"+14.74%":[137],"mIoU.":[139,164],"Potsdam,":[141],"MeCSAFNet-large":[142],"improves":[144],"over":[145,171],"DeepLabV3+":[146,150],"+6.48%,":[149],"+5.85%,":[153],"+9.11%,":[157],"+4.80%":[162],"also":[167],"achieves":[168],"consistent":[169],"gains":[170],"several":[172],"recent":[173],"state-of-the-art":[174],"approaches.":[175],"Moreover,":[176],"compact":[177],"variants":[178],"of":[179],"MeCSAFNet":[180],"deliver":[181],"notable":[182],"lower":[185],"training":[186],"time":[187],"reduced":[189],"inference":[190],"cost,":[191],"supporting":[192],"their":[193],"deployment":[194],"resource-constrained":[196],"environments.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-13T00:00:00"}
