Self-Supervised Learning Hyperspectral Image Denoiser with Separated Spectral-Spatial Feature Extraction
Honglin Zhu, Minchao Ye, Yi Qiu, Yuntao Qian · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Deep learning-based methods have achieved remarkable results in the field of hyperspectral image (HSI) denoising, and these methods are typically trained on pairs of noisy input and clean target images. How to deal with the noise in real-world HSIs when clean targets are unavailable is still a challenging problem. In this paper, we propose a self-supervised HSI denoiser in which only a single noisy HSI is utilized. We exploit the blind-spot network and extend the method in the spatial-spectral space to accomplish self-supervised learning. In order to better extract spatial-spectral features with limited training samples, we use separable feature extraction modules to extract spectral-spatial joint information of HSI separately and finally fuse these features. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms some state-of-the-art denoising approaches.