关键词: 超光谱图像, SG滤波, 中值滤波, 空谱网络（SSN）

Abstract:

Recently, many deep learning methods are used in hyperspectral image (HSI) pixel classification task. However, spectral noise and spatial noise still exist in HSI simultaneously even with a series of preprocessing. Most of these deep learning methods only involve the noise filtering in spatial domain while paying no attention to that in spectral domain. So spectral noise is preserved in deep features after deep feature learning process, which   degrades the pixel classification accuracy. Given the excellent classification accuracy of spectral-spatial network (SSN), this paper proposes two deep feature learning models fused spectral noise filtering with it, called Saviztky-Galoy filtering SSN (SG-SSN) and median filtering SSN (MF-SSN), respectively. The models remove spectral noise by using Saviztky- Galoy filtering or median filtering on spectral domain first. Then, the denoised image is fed into SSN to learn deep features of HSI for its excellent classification effects. Experiments on two popular HSI datasets demonstrate the effectiveness of SG-SSN and MF-SSN, and classification result is measured by overall accuracy (OA), average accuracy (AA) and Kappa coefficient. How the appropriate layer number of model affects model performance is also discussed.

Key words: hyperspectral image, SG filter, median filter, spectral-spatial network (SSN)