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.

我们介绍了 HyLITE，这是一种模型局部和光谱信息的视觉转换器，利用新型的正则化函数促进了局部到全局信息的整合，该方法在准确度上超过了竞争基准，并取得了高达 10％的增益。