MIFOMO adapts a remote sensing foundation model with coalescent projection, mixup domain adaptation, and label smoothing to outperform prior methods by up to 14% in cross-domain few-shot HSI classification.
Airborne hyperspectral data over chikusei
2 Pith papers cite this work. Polarity classification is still indexing.
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SDANet achieves state-of-the-art hyperspectral image super-resolution performance on two benchmark datasets by dynamically sparsifying channel attention and jointly modeling spatial-frequency representations.
citing papers explorer
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Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model
MIFOMO adapts a remote sensing foundation model with coalescent projection, mixup domain adaptation, and label smoothing to outperform prior methods by up to 14% in cross-domain few-shot HSI classification.
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Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
SDANet achieves state-of-the-art hyperspectral image super-resolution performance on two benchmark datasets by dynamically sparsifying channel attention and jointly modeling spatial-frequency representations.