DSCC groups spectrally similar and spatially close pixels into supertokens using multi-criteria distance and soft labels, then classifies at the token level to achieve 0.728 CF1 at 197.75 FPS on WHU-OHS.
Spectralformer: Rethinking hyperspectral image classifica- tionwithtransformers.IEEETransactionsonGeoscienceandRemote Sensing 60, 1–15
2 Pith papers cite this work. Polarity classification is still indexing.
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A new CNN-Transformer hybrid with twin-branch 3D/2D convolution, hybrid pooling attention, cascade spectral transformers, and cross-layer fusion reports higher accuracy than prior methods on standard hyperspectral datasets.
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Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
DSCC groups spectrally similar and spatially close pixels into supertokens using multi-criteria distance and soft labels, then classifies at the token level to achieve 0.728 CF1 at 197.75 FPS on WHU-OHS.
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A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification
A new CNN-Transformer hybrid with twin-branch 3D/2D convolution, hybrid pooling attention, cascade spectral transformers, and cross-layer fusion reports higher accuracy than prior methods on standard hyperspectral datasets.