{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-EWBEQS69/461ca079-b9d1-466c-86e7-077ae5af6f2c/PDF","dcterms:extent":"771 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-EWBEQS69/b5126995-9af1-4494-bbde-aec53ab5d79d/TEXT","dcterms:extent":"0 KB"}],"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-EWBEQS69","dcterms:issued":"2026","dc:creator":["Attaf, Dalila","Boumediene, Kadda","Haffaf, Hafid"],"dc:format":[{"@xml:lang":"sl","#text":"številka:4"},{"@xml:lang":"sl","#text":"letnik:93"},{"@xml:lang":"sl","#text":"str. 206-215"}],"dc:identifier":["ISSN:0013-5852","COBISSID_HOST:301650179","URN:URN:NBN:SI:doc-EWBEQS69"],"dc:language":"sl","dc:publisher":{"@xml:lang":"sl","#text":"Elektrotehniška zveza Slovenije"},"dc:source":{"@xml:lang":"sl","#text":"Elektrotehniški vestnik"},"dc:subject":[{"@xml:lang":"en","#text":"Presence and Background Learning (PBL)"},{"@xml:lang":"en","#text":"pseudo labelling"},{"@xml:lang":"en","#text":"remote sensing images"},{"@xml:lang":"en","#text":"self-training"},{"@xml:lang":"en","#text":"semi-supervised learning"},{"@xml:lang":"en","#text":"Sentinel-2"},{"@xml:lang":"en","#text":"water body extraction"}],"dc:title":{"@xml:lang":"sl","#text":"Confidence-driven background selection for presence and background learning in remote sensing water body mapping|"},"dc:description":[{"@xml:lang":"sl","#text":"The Presence and Background Learning (PBL) framework has demonstrated a robust performance for Semi-Supervised Learning (SSL) techniques in remote sensing, particularly in case of the scarce labelled data. However, it’s a conventional reliance on random sampling to form the background set introduces a critical vulnerability: random selection may inadvertently include pixels from the target class, thereby corrupting the background distribution and degrading the model adjustment. In our paper, we propose a novel modified PBL algorithm that replaces the random background sampling with a confidence-driven pseudo-label selection strategy. Precisely, our naive PBL model is first trained on a small set of labelled positive samples and a preliminary set of unlabelled samples; its predicted probability scores are then used to filter the full unlabelled pool; only samples with a low presence probability, those unlikely to belong to the target class, are retained as the background. This produces a cleaner, more semantically coherent background distribution, thereby improving the model training. Applied to a water body extraction from a Sentinel-2 imagery over the ecologically complex of the El Kala National Park (Algeria), our method consistently outperforms the naive PBL baseline across all evaluation metrics, including the Kappa coefficient and F1-score"},{"@xml:lang":"sl","#text":"Metoda učenja prisotnosti in ozadja (PBL) je učinkovita pri polnadzorovanem učenju z omejenim številom označenih podatkov, zlasti pri daljinskem zaznavanju. Vendar lahko naključno izbiranje vzorcev ozadja povzroči vključitev pikslov ciljanega razreda, kar poslabša učenje modela. V prispevku predstavljamo izboljšano metodo PBL, ki naključno vzorčenje nadomešča z izbiro na podlagi zaupanja v napovedi modela. Ta se najprej nauči prepoznavati označene pozitivne vzorce, nato pa z oceno verjetnosti prisotnosti izbere neoznačene vzorce, ki z majhno verjetnostjo pripadajo ciljnemu razredu. Tako oblikovano ozadje omogoča zanesljivejše učenje. Metodo smo preizkusili pri zaznavanju vodnih teles na satelitskih posnetkih Sentinel-2 ekološko raznolikega parka El Kala v Alžiriji. Rezultati kažejo, da predlagani pristop pri vseh uporabljenih metrikah, vključno s koeficientom Kappa in mero F1, presega osnovno metodo PBL"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:DOC-EWBEQS69","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-EWBEQS69"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-EWBEQS69/461ca079-b9d1-466c-86e7-077ae5af6f2c/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Elektrotehniška zveza Slovenije"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:DOC-EWBEQS69/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-EWBEQS69"}}}}