Papers › Locality-Aware Hyperspectral Classification

Locality-Aware Hyperspectral Classification

4 Sep 2023arXiv:2309.01561archive 2025-07-28

Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren

Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wide continuum of spectra. Researchers have been working on automating Hyperspectral image classification, with recent efforts leveraging Vision-Transformers. However, most research models only spectra information and lacks attention to the locality (i.e., neighboring pixels), which may be not sufficiently discriminative, resulting in performance limitations. To address this, we present three contributions: i) We introduce the Hyperspectral Locality-aware Image TransformEr (HyLITE), a vision transformer that models both local and spectral information, ii) A novel regularization function that promotes the integration of local-to-global information, and iii) Our proposed approach outperforms competing baselines by a significant margin, achieving up to 10% gains in accuracy. The trained models and the code are available at HyLITE.

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Code

zhoufangqin/hylite officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Classification Hyperspectral Image Classification Image Classification image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Houston	HyLITE	OA@15perclass	88.49	#4 of 4	Archive leaderboard	report
Hyperspectral Image Classification	Indian Pines	HyLITE	OA@15perclass	89.80	#3 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Indian Pines	HyLITE	Overall Accuracy	89.80	#3 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	HyLITE	OA@15perclass	91.28	#3 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Attention Dense Connections Layer Normalization Linear Layer Multi-Head Attention Residual Connection Softmax Vision Transformer

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