Decs-Net: Convolutional Self-Encoding Network for Hyperspectral Image Denoising

Noises in hyperspectral image (HSI) degrades both spatial and spectral features of ground objects, and greately defects the following processing, such as classification, target detection and recognition. In this paper, a convolutional self-encoding network (DeCS-Net) is designed for HSI denoising, which integrates the superiority of convolutional neural network (CNN) and auto-encoder (AE) to learn multi-scale features. The noise in the observed HSI is estimated by residual learning strategy, and is removed from the observed HSI to obtain an estimation of the ideal HSI without noise. Experimental results on benchmark HSI data set illustrate that the proposed DeCS-Net is effective for HSI denoising and outperforms the state-of-the-art CNN based HSI denoising methods.