Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
Hyperspectral image classification demands spatially coherent predictions and precise boundary delineation. Yet prevailing superpixel-based methods face an inherent contradiction: clustering aggregates similar pixels into regions, but the subsequent classifier operates pixel-wise, undermining regional consistency. Consequently, existing approaches do not guarantee region-level, boundary-aligned classification. To address this limitation, we propose the Dual-stage Spectrum-Constrained Clustering-based Classifier (DSCC), an end-to-end framework that explicitly decouples clustering from classification by first grouping spectral similar and spatially proximate pixels into spectral supertokens...
| Authors | Peifu Liu, Tingfa Xu, Jie Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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