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    dct:creator orcid:0000-0001-8884-9743, orcid:0009-0001-1115-9741, orcid:0009-0008-3666-303X;
    dct:publisher <https://ror.org/045k5vt03>;
    dct:subject <http://purl.obolibrary.org/obo/NCIT_C19349>;
    rdfs:comment "Prescribed burns help reduce wildfire risk, yet assessing post-burn vegetation recovery remains difficult due to the high dimensionality and labeling cost of hyperspectral imagery (HSI). We propose BurnSSL-DRL, a label-efficient framework that couples self-supervised learning (SSL) with deep reinforcement learning (DRL) for spectral band selection and vegetation classification. The DRL agent prioritized low-wavelength VNIR regions linked to chlorophyll degradation and soil exposure, reducing dimensionality to 30 bands while retaining key information. When combined with a 3D spectral–spatial CNN and class-balancing strategies (SMOTE + weighted loss), the BurnSSL-DRL achieved a macro-F1 ≈ 0.52—about 4–6% higher than PCA and mRMR baselines—and improved minority-class F1 (Grass 0.02 → 0.30, Soil 0.40 → 0.65). These results demonstrate that BurnSSL-DRL enables compact, interpretable, and accurate post-burn vegetation mapping, supporting scalable and near-real-time ecological monitoring from UAV platforms.";
    rdfs:label "Hyperspectral band selection via self-supervised and reinforcement learning for prescribed burn impact analysis";
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    <https://www.w3.org/ns/dcat#contactPoint> "bradley.whitaker1@montana.edu";
    <https://www.w3.org/ns/dcat#endDate> "15 Dec 2025";
    <https://www.w3.org/ns/dcat#startDate> "2024" .
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  orcid:0009-0008-8411-2742 foaf:name "Emily Regalado" .
  
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