. . . . . . . . . . . "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." . "Hyperspectral band selection via self-supervised and reinforcement learning for prescribed burn impact analysis" . . . "bradley.whitaker1@montana.edu" . "15 Dec 2025" . "2024" . . "Emily Regalado" . "2026-06-15T19:19:57.966Z"^^ . . . . . . . . . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB" . "ShNJ5c2HO2lS8FaJ/xVzjA4o/cuz/DTp2MtZ/L0XumqSbEwcm1wqFP4Hqx5Qt84RX3mH5tujVGq33NnMASmNP3XcSe8xmQG7Nhrf5A9y52X4NOcmg8yJ6gJWLfVRFNjSfCN1P1z0Z9qZPTaVdTzUrExgF/AkRuFIuryR/tPoFo/f6+6iEvptwbRFitLVHq/JXEpFFTcr+R62Fgt/U1JQ/UWgHT9DeBzkD7g6XNbbgpqNbQIvnInjzEXgB3lU9JTrCDPvAqpHhGOfNPu2N99K1JvDSfojqxUKSaKaDm3FnPG4pWT2LrvfbqhwbXbB8kmouZtkNL6Ar1dltDUNbdPvpQ==" . . .