SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with advanced backbone networks.

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• 1 Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification.
• 2 However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone.
• 3 To solve this issue, we rethink HS image classification from a sequential perspective with transformers, and propose a novel backbone network called \ul{SpectralFormer}.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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