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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Huanfeng Shen Xinghua Li Qing Cheng Chao Zeng Gang Yang Huifang Li Liangpei Zhang |
| Copyright Year | 2013 |
| Abstract | Because of sensor malfunction and poor atmospheric conditions, there is usually a great deal of missing information in optical remote sensing data, which reduces the usage rate and hinders the follow-up interpretation. In the past decades, missing information reconstruction of remote sensing data has become an active research field, and a large number of algorithms have been developed. However, to the best of our knowledge, there has not, to date, been a study that has been aimed at expatiating and summarizing the current situation. This is therefore our motivation in this review. This paper provides an introduction to the principles and theories of missing information reconstruction of remote sensing data. We classify the established and emerging algorithms into four main categories, followed by a comprehensive comparison of them from both experimental and theoretical perspectives. This paper also predicts the promising future research directions. |
| Starting Page | 61 |
| Ending Page | 85 |
| Page Count | 25 |
| File Size | 8066952 |
| File Format | |
| ISSN | 21686831 |
| Volume Number | 3 |
| Issue Number | 3 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Remote sensing Image reconstruction Data integration Interpolation Three-dimensional displays Atmospheric measurements |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Instrumentation Computer Science Electrical and Electronic Engineering |
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