太阳成8722(中国)有限公司-GREEN NO.1

标题：Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information作者：Xinghua Li, Huanfeng Shen, Liangpei Zhang, Huifang Li

来源出版物：ISPRS Journal of Photogrammetry and Remote Sensing 卷：106 页：1-15 DOI：doi:10.1016/j.isprsjprs.2015.03.009 出版年：August 2015

摘要：Because of sensor failure and poor observation conditions, remote sensing (RS) images are easily subjected to information loss, which hinders our effective analysis of the earth. As a result, it is of great importance to reconstruct the missing information (MI) of RS images. Recent studies have demonstrated that sparse representation based methods are suitable to fill large-area MI. Therefore, in this paper, we investigate the MI reconstruction of RS images in the framework of sparse representation. Overall, in terms of recovering the MI, this paper makes three major contributions: (1) we propose an analysis model for reconstructing the MI in RS images; (2) we propose to utilize both the spectral and temporal information; and (3) on this basis, we make a detailed comparison of the two kinds of sparse representation models (synthesis model and analysis model). In addition, experiments were conducted to compare the sparse representation methods with the other state-of-the-art methods.

文献类型：Article

语种：English

作者关键词：Analysis model; Missing information (MI); Remote sensing (RS); Sparse representation; Spectral and temporal information; Synthesis model

通讯作者地址：Huanfeng Shen,School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei Province, China

电子邮件地址：shenhf@whu.edu.cn

地址：

[Xinghua Li, Huanfeng Shen, Huifang Li]School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei Province, China

[Huanfeng Shen, Liangpei Zhang]Collaborative Innovation Center for Geospatial Information Technology, Wuhan University, Wuhan, Hubei Province, China

[Liangpei Zhang]The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, Hubei Province, China

ISSN：0924-2716

全文链接：http://www.sciencedirect.com/science/journal/09242716