Satellite Data Reduction Using Entropy-preserved Image Compression Technique

Li Jun; Zhou Fengxian; Gao Qinghuai

doi:10.1007/BF02658097

Impact Factor: 5.8

Volume 8 Issue 2

Mar. 1991

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1991 > 8(2): 237-242

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

1.
Institute of Atmospheric Physics, Academia Sinica, Beijing 100029, China,Institute of Atmospheric Physics, Academia Sinica, Beijing 100029, China,Information Science Center, Peking University, Beijing 100871, China

doi: 10.1007/BF02658097

Abstract
References
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Abstract:
In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.
References

[1]	Li Jun, Zhou Fengxian, Gao Qinghuai, 1990: Delineation of Mesoscale Features of Ocean on Satellite IR Image, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 423-432. doi: 10.1007/BF03008872
[2]	LI Pingyang, JIANG Weimei, SUN Jianning, YUAN Renmin, 2003: A Laboratory Modeling of the Velocity Field in the Convective Boundary Layer with the Particle Image Velocimetry Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 631-637. doi: 10.1007/BF02915506
[3]	Li Jun, Zhou Fengxian, 1992: On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 373-382. doi: 10.1007/BF02656948
[4]	Seung-Woo LEE, Dong-Kyou LEE, 2011: Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 758-774. doi: 10.1007/s00376-010-0145-6
[5]	Shengzhe CHEN, Jiping LIU, Yifan DING, Yuanyuan ZHANG, Xiao CHENG, Yongyun HU, 2021: Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 168-186. doi: 10.1007/s00376-020-0213-5
[6]	Yan Shaojin, Peng Yongqing, Wang Jianzhong, 1991: Determination of Kolmogorov Entropy of Chaotic Attractor Included in One-Dimensional Time Series of Meteorological Data, ADVANCES IN ATMOSPHERIC SCIENCES, 8, 243-250. doi: 10.1007/BF02658098
[7]	P.C.S. Devara, P. Ernest Raj, 1992: Atmospheric NO2 Concentration Measurements Using Differential Absorption Lidar Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 73-82. doi: 10.1007/BF02656932
[8]	Kefeng ZHU, Ming XUE, Yujie PAN, Ming HU, Stanley G. BENJAMIN, Stephen S. WEYGANDT, Haidao LIN, 2019: The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1308-1326. doi: 10.1007/s00376-019-9011-3
[9]	Fang Xianjin, 1992: Spectral and Anisotropic Corrections for GMS Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 287-298. doi: 10.1007/BF02656939
[10]	Peng ZHANG, Qifeng LU, Xiuqing HU, Songyan GU, Lei YANG, Min MIN, Lin CHEN, Na XU, Ling Sun, Wenguang BAI, Gang MA, Di XIAN, 2019: Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1027-1045. doi: 10.1007/s00376-019-8215-x
[11]	ZENG Heqing, JIA Gensuo, 2013: Impacts of Snow Cover on Vegetation Phenology in the Arctic from Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1421-1432. doi: 10.1007/s00376-012-2173-x
[12]	Li Jun, Wang Luyi, Zhou Fengxian, 1993: Convective and Stratiform Cloud Rainfall Estimation from Geostationary Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 475-480. doi: 10.1007/BF02656972
[13]	Ruiyao CHEN, Ralf BENNARTZ, 2021: Rainfall Algorithms Using Oceanic Satellite Observations from MWHS-2, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1367-1378. doi: 10.1007/s00376-020-0258-5
[14]	LIU Xiaoyang, MAO Jietai, ZHU Yuanjing, LI Jiren, 2003: Runoff Simulation Using Radar and Rain Gauge Data, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 213-218. doi: 10.1007/s00376-003-0006-7
[15]	Michael B. RICHMAN, Lance M. LESLIE, Theodore B. TRAFALIS, Hicham MANSOURI, 2015: Data Selection Using Support Vector Regression, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 277-286. doi: 10.1007/s00376-014-4072-9
[16]	Yaodeng CHEN, Jie SHEN, Shuiyong FAN, Deming MENG, Cheng WANG, 2020: Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1222-1238. doi: 10.1007/s00376-020-0080-0
[17]	Keyi CHEN, Niels BORMANN, Stephen ENGLISH, Jiang ZHU, 2018: Assimilation of Feng-Yun-3B Satellite Microwave Humidity Sounder Data over Land, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 268-275. doi: 10.1007/s00376-017-7088-0
[18]	Jincheng WANG, Xingwei JIANG, Xueshun SHEN, Youguang ZHANG, Xiaomin WAN, Wei HAN, Dan WANG, 2023: Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 44-61. doi: 10.1007/s00376-022-1349-2
[19]	Fabien CARMINATI, Nigel ATKINSON, Brett CANDY, Qifeng LU, 2021: Insights into the Microwave Instruments Onboard the Fengyun 3D Satellite: Data Quality and Assimilation in the Met Office NWP System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1379-1396. doi: 10.1007/s00376-020-0010-1
[20]	Bohua Huang, James L. Kinter III, Paul S. Schopf, 2002: Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 965-992. doi: 10.1007/s00376-002-0059-z

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Manuscript History

Manuscript received: 10 March 1991

Manuscript revised: 10 March 1991

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

1. Institute of Atmospheric Physics, Academia Sinica, Beijing 100029, China,Institute of Atmospheric Physics, Academia Sinica, Beijing 100029, China,Information Science Center, Peking University, Beijing 100871, China

Manuscript received: 1991-03-10
Manuscript revised: 1991-03-10

Abstract: In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.