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Locality-Aware Hyperspectral Classification

Fangqin Zhou (Technology University of Eindhoven), Mert Kilickaya (Eindhoven University of Technology),* Joaquin Vanschoren (Eindhoven University of Technology)

The 34^th British Machine Vision Conference

Abstract

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.

Video

Citation

@inproceedings{Zhou_2023_BMVC,
author    = {Fangqin Zhou and Mert Kilickaya and Joaquin Vanschoren},
title     = {Locality-Aware Hyperspectral Classification},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {https://papers.bmvc2023.org/0022.pdf}
}

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