HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

Blog Article

The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective.However, RGB-based networks do not take full advantage of the spectral information in hyperspectral data.This inspired the creation of HyperKon, a dedicated hyperspectral Convolutional Neural Network backbone built with self-supervised contrastive representation learning.

HyperKon uniquely leverages the kuza growth premium oil high spectral whirlwind multi selector continuity, range, and resolution of hyperspectral data through a spectral attention mechanism.We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers.Notably, HyperKon achieves a remarkable 98% Top-1 retrieval accuracy and surpasses traditional RGB-trained backbones in both pansharpening and image classification tasks.

These results highlight the potential of hyperspectral-native backbones and herald a paradigm shift in hyperspectral image analysis.