Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering

• URL: http://arxiv.org/abs/2604.27364v1
• Date: Thu, 30 Apr 2026 03:20:37 GMT
• Title: Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
• Abstract summary: Hyperspectral image classification demands spatially coherent predictions and precise boundary delineation.<n>Superpixel-based methods face an inherent contradiction: clustering aggregates similar pixels into regions, but the subsequent pixel-wise operates pixel-wise, undermining regional consistency.<n>We propose the Dual-Constrained Clustering, which explicitly decouples clustering from classification by grouping similar pixels into spectral supertokens.
• Score: 27.81669681803444
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 and then performing token-level prediction. At its core, DSCC computes an image-level multi-criteria feature distance between pixels and centers, followed by a locality-aware assignment regularization, enabling the generation of boundary-preserving spectral supertokens. A density-isolation based center selection further yields representative, well-separated centers, reducing redundancy and improving robustness to scale variation. To accommodate mixed land-cover compositions within each token, we introduce a soft-label scheme that encodes class proportions and improves robustness for mixed-class tokens. DSCC attains a CF1 of 0.728 at 197.75 FPS on the WHU-OHS dataset, offering a superior accuracy-efficiency trade-off compared with state-of-the-art methods. Extensive experiments further validate the effectiveness and generality of the proposed dual-stage paradigm for hyperspectral image classification. The source code is available at https://github.com/laprf/DSCC.

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