@inproceedings{e0b53d2415994f8d9d5625c182800c56,
title = "Remote Sensing Image Compression: A Review",
abstract = "With the increasing spatial and temporal resolutions of acquired remote sensing (RS) images, effective image compression is becoming more and more important. RS image compression technologies have been extensively studied in the past a few decades, and various algorithms have been developed accordingly. In this paper, we provide an overview of practically deployed RS image compression approaches, including predictive coding and transform coding approaches that have been adopted in different satellite systems. In addition, some newly derived RS image compression methods are discussed, with highlights on the new trends of the on-going design and developments of RS image compression.",
keywords = "Remote sensing, compressed sensing, image compression, predictive coding, task-driven coding, transform",
author = "Shichao Zhou and Chenwei Deng and Baojun Zhao and Yatong Xia and Qisheng Li and Zhenzhong Chen",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 1st IEEE International Conference on Multimedia Big Data, BigMM 2015 ; Conference date: 20-04-2015 Through 22-04-2015",
year = "2015",
month = jul,
day = "9",
doi = "10.1109/BigMM.2015.16",
language = "English",
series = "Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "406--410",
booktitle = "Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015",
address = "United States",
}