.png){kind=icon}
返回
A Tutorial on Image Compression for Optical Space Imaging Systems
.png){kind=icon}
OA
AI
DOI：10.1109/MGRS.2014.2352465.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Public policies and private initiatives share the will to explore outer space and to monitor the Earth from space sensors. Recent years have seen an increased number of space missions, while the sensors on board aircrafts or spacecrafts have also significantly improved their acquisition capabilities. Given this huge volume of remote sensing data and the detailed characteristics of the acquired images, a data compression process is in order to allow as large a transmission rate as possible. In this paper we provide an overview of several standards for remote sensing data compression, notably of those recently approved by the Consultative Committee for Space Data Systems, although the use of other ISO/IEC image coding standards is also dealt with. Discussion embraces both mono band and multi band compression, and lossless, lossy and near-lossless compression. Illustrative results are reported for a set of AVIRIS and Hyperion images, indicating that exploiting the spectral correlation-either in prediction-based or in transform-based schemes-is paramount to achieve improved coding performance.
Keyword:
NEAR-LOSSLESS COMPRESSION
TRANSFORM
ALGORITHM
LOSSY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
Performance indicators nomenclatures for decision making in sustainable conditions based maintenanceOAAI
.png){kind=icon}
OAAI分享收藏分享收藏Optimized Onboard Lossless and Near-Lossless Compression of Hyperspectral Data Using CALIC使用CALIC优化高光谱数据的机载无损和近无损压缩
PREAI
分享收藏
查看更多内容