AI RESEARCH
Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
arXiv CS.CV
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ArXi:2604.27364v1 Announce Type: new 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.