771 KB0 KB2026Attaf, DalilaBoumediene, KaddaHaffaf, Hafidštevilka:4letnik:93str. 206-215ISSN:0013-5852COBISSID_HOST:301650179URN:URN:NBN:SI:doc-EWBEQS69slElektrotehniška zveza SlovenijeElektrotehniški vestnikPresence and Background Learning (PBL)pseudo labellingremote sensing imagesself-trainingsemi-supervised learningSentinel-2water body extractionConfidence-driven background selection for presence and background learning in remote sensing water body mapping|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-scoreMetoda 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 PBLTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaElektrotehniška zveza Slovenije