Self-Supervised Calibration of the Denoising Networks for HSI

Typically, neural networks are trained using supervised learning (SL) and evaluated on unseen data. This type of training relies on a substantial amount of data, including clean images. However, in the case of hyperspectral images (HSIs), acquiring a large number of images along with clean versions can be challenging and expensive. This study proposes a two-stage learning strategy to train the model for HSI data with previously unseen noise patterns. The first stage involves supervised learning to train the model on noisy and clean data pairs. The second stage incorporates self-supervised calibration using only noisy data to adapt the model to specific noise patterns. For the latter, to estimate the middle spectral band, we leverage the information from its neighboring band as a target. To ensure the network learns meaningful relationships rather than merely copying the input, we strategically create a blind spot by excluding the target band from the input data. Therefore, our self-supervised learning technique is named as Blind Band Self-Supervised (BBSS) Learning. Our approach has been shown to improve the accuracy of the model for noisy HSIs, even when the network did not previously encounter the specific noise patterns in SL.