Hyperspectral Knowledge-Guided Cross-Sensor Burned-Area Mapping: Distilling AVIRIS Semantics to Sentinel-2 and Landsat-8
TL;DR
A task-oriented hyperspectral-to-multispectral semantic-distillation framework in which bitemporal airborne visible/infrared imaging spectrometer (AVIRIS) observations supervise deployable multispectral students is proposed, which support AVIRIS-guided semantic transfer when paired training records are available, while broader validation, hard-scene stabilization, and dedicated evaluation of the optional simulated pathway remain necessary.
Abstract
Accurate burned-area mapping is essential for postfire assessment, vegetation recovery monitoring, carbon-emission estimation, and ecological-restoration planning. Multispectral satellites enable scalable burned-area monitoring, but limited spectral resolution can confound fire effects with agricultural disturbance, exposed soil, shadows, and other nonfire changes. This study proposes a task-oriented hyperspectral-to-multispectral semantic-distillation framework in which bitemporal airborne visible/infrared imaging spectrometer (AVIRIS) observations supervise deployable multispectral students. The evaluated real-target pathway maps AVIRIS samples to Landsat-8 or Sentinel-2 pixels and trains the student with blended hard labels and teacher probabilities. An optional spectral-response-function-only pathway is formulated for cases without paired target observations but is not quantitatively evaluated. Under grouped leave-one-scene-out evaluation on five Landsat-8 scenes, the strongest 101-seed pairing—a transformer teacher and a multilayer perceptron (MLP) student—achieved 89.17 ± 1.99% mean F1-score and 81.05 ± 2.92% burned-class intersection over union (IoU), improving direct MLP by 1.64 and 2.22 percentage points. The compact MLP-to-MLP pairing also improved its direct counterpart. On three Sentinel-2 scenes, this compact pairing improved direct MLP by 3.82 F1 points and 5.53 IoU points, whereas Mamba-style results showed architecture-dependent gains. Independent evaluation across Australia, Chile, and Mediterranean islands showed that the lightweight student achieved competitive precision–recall performance without regional retraining, with band-angle-index random forest and Rao’s Q as event-level comparators and the global annual burned-area map as an annual product-level reference. The results support AVIRIS-guided semantic transfer when paired training records are available, while broader validation, hard-scene stabilization, and dedicated evaluation of the optional simulated pathway remain necessary.