@inproceedings{5ce334bd3e844573aad515cca0a5f018,
title = "Decs-net: Convolutional self-encoding network for hyperspectral image denoising",
abstract = "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 selfencoding 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.",
keywords = "Convolutional neural network (CNN), Deep learning, Denoising, Hyperspectral image, Restoration",
author = "Xiao Liu and Shaohui Mei and Zhi Zhang and Yifan Zhang and Jingyu Ji and Qian Du",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
doi = "10.1109/IGARSS.2019.8900642",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1951--1954",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
}