  The exceptional spectral resolution of hyperspectral imagery enables material
insights that are not possible with RGB or multispectral images. Yet, the full
potential of this data is often underutilized by deep learning techniques due
to the scarcity of hyperspectral-native CNN backbones. To bridge this gap, we
introduce HyperKon, a self-supervised contrastive learning network designed and
trained on hyperspectral data from the EnMAP Hyperspectral
Satellite\cite{kaufmann2012environmental}. HyperKon uniquely leverages the high
spectral continuity, range, and resolution of hyperspectral data through a
spectral attention mechanism and specialized convolutional layers. We also
perform a thorough ablation study on different kinds of layers, showing their
performance in understanding hyperspectral layers. It achieves an outstanding
98% Top-1 retrieval accuracy and outperforms traditional RGB-trained backbones
in hyperspectral pan-sharpening tasks. Additionally, in hyperspectral image
classification, HyperKon surpasses state-of-the-art methods, indicating a
paradigm shift in hyperspectral image analysis and underscoring the importance
of hyperspectral-native backbones.
