  Hyperspectral image (HSI) classification is challenging due to spatial
variability caused by complex imaging conditions. Prior methods suffer from
limited representation ability, as they train specially designed networks from
scratch on limited annotated data. We propose a tri-spectral image generation
pipeline that transforms HSI into high-quality tri-spectral images, enabling
the use of off-the-shelf ImageNet pretrained backbone networks for feature
extraction. Motivated by the observation that there are many homogeneous areas
with distinguished semantic and geometric properties in HSIs, which can be used
to extract useful contexts, we propose an end-to-end segmentation network named
DCN-T. It adopts transformers to effectively encode regional adaptation and
global aggregation spatial contexts within and between the homogeneous areas
discovered by similarity-based clustering. To fully exploit the rich spectrums
of the HSI, we adopt an ensemble approach where all segmentation results of the
tri-spectral images are integrated into the final prediction through a voting
scheme. Extensive experiments on three public benchmarks show that our proposed
method outperforms state-of-the-art methods for HSI classification.
