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Volume 8 Issue 2

Mar.  1991

Article Contents

Satellite Data Reduction Using Entropy-preserved Image Compression Technique


doi: 10.1007/BF02658097

  • 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.
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    [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] 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
    [4] 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
    [5] 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
    [6] 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
    [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] Fang Xianjin, 1992: Spectral and Anisotropic Corrections for GMS Satellite Data, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 287-298.  doi: 10.1007/BF02656939
    [9] 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
    [10] 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
    [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] 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
    [13] 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
    [14] 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
    [15] BAI Yulong, LI Xin, and HUANG Chunlin, 2013: Handling error propagation in sequential data assimilation using an evolutionary strategy, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1096-1105.  doi: 10.1007/s00376-012-2115-7
    [16] YAO Zhigang, LIN Longfu, CHEN Hongbin, FEI Jianfang, 2008: A Scheme for Estimating Tropical Cyclone Intensity Using AMSU-A Data, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 96-106.  doi: 10.1007/s00376-008-0096-3
    [17] Xiangxiang ZHANG, Yongjiu DAI, Hongzhi CUI, Robert E. DICKINSON, Siguang ZHU, Nan WEI, Binyan YAN, Hua YUAN, Wei SHANGGUAN, Lili WANG, Wenting FU, 2017: Evaluating Common Land Model Energy Fluxes Using FLUXNET Data, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1035-1046.  doi: 10.1007/s00376-017-6251-y
    [18] 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
    [19] Zou Han, Gao Yongqi, 1997: Vertical Ozone Profile over Tibet Using Sage I and II Data, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 505-512.  doi: 10.1007/s00376-997-0068-z
    [20] FAN Xuehua, CHEN Hongbin, LIN Longfu, HAN Zhigang, Philippe GOLOUB, 2009: Retrieval of Aerosol Optical Properties over the Beijing Area Using POLDER/PARASOL Satellite Polarization Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1099-1107.  doi: 10.1007/s00376-009-8103-x

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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
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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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

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.

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