Locality-Aware Hyperspectral Classification

TL;DR

This paper introduces HyLITE, a vision transformer that models both local pixel neighborhoods and spectral data in hyperspectral images, significantly improving classification accuracy over existing methods.

Contribution

The paper presents HyLITE, a novel hyperspectral image transformer that incorporates locality awareness and a new regularization to enhance spectral and spatial feature integration.

Findings

Achieves up to 10% accuracy improvement over baselines

Effectively models local and spectral information simultaneously

Provides open-source code and trained models

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. The trained models and the code are available at HyLITE.