SURE BASED CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL IMAGE DENOISING

被引：5

作者：

Nguyen, Han, V ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

机构：

[1] Univ Iceland, Fac Elect & Comp Engn, Reykjavik, Iceland

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

Hyperspectral image denoising; unsupervised deep learning; convolutional neural network; Stein's unbiased risk estimate;

D O I：

10.1109/IGARSS39084.2020.9324734

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses the hyperspectral image (HSI) denoising problem by using Stein's unbiased risk estimate (SURE) based convolutional neural network (CNN). Conventional deep learning denoising approaches often use supervised methods that minimize a mean-squared error (MSE) by training on noisy-clean image pairs. In contrast, our proposed CNN-based denoiser is unsupervised and only makes use of noisy images. The method uses SURE, which is an unbiased estimator of the MSE, that does not require any information about the clean image. Therefore minimization of the SURE loss function can accurately estimate the clean image only from noisy observation. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms competitive HSI denoising methods.

引用

页码：1484 / 1487

页数：4