Dynamic Super-Pixel Normalization for Robust Hyperspectral Image Classification

Deep neural networks (DNNs) have underpinned most of recent progress of hyperspectral image (HSI) classification. One premise of their success lies on the high image quality without noise corruption. However, due to the limitation of imaging sensor and imaging conditions, HSIs captured in practice inevitably suffer from random noise, which will degrade the generalization performance and robustness of most existing DNN based methods. In this study, we propose a dynamic super-pixel normalization based DNN for HSI classification, which can adaptively relieve the negative effect caused by various types of noise corruption and improve the generalization performance. To achieve this goal, we propose a dynamic super-pixel normalization module, for a given super-pixel which normalizes the inner pixel features using parameters dynamically generated based on themselves. By doing this, such a module enables adaptively restoring the similarity among pixels within the super-pixel corrupted by random noise through aligning their feature distribution, thus enhancing the generalization performance on noisy HSI. Moreover, it can be directly plugged into any other existing DNN architectures. To appropriately train the proposed DNN model, we further present a semi-supervised learning framework, which integrates the cross entropy loss and Kullback-Leibler divergence loss on labeled samples with the infomation entropy loss on the unlabeled samples for joint learning to well sidestep over-fitting. Experiments on three benchmark HSI classification datasets demonstrate the advantages of the proposed method over several state-of-the-art competitors in handling HSIs under different types of noise corruption.