Research on Algorithm of Cloud Detection for HJ-1B Image Based on Dynamical Thresholding

doi:10.6046/gtzyyg.2012.02.03

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Abstract Through analyzing the annual volatility of cloud detection thresholds and integrating the band characteristics of HJ-1B,the authors proposed a new algorithm of cloud detection for HJ-1B image based on dynamical thresholding according to the spectral standard deviation anomaly. Using image registration,band math,linear regression and error analysis,the authors acquired the cloud abnormal regions which could be used to remove cloud pixels from the image. The results show that this approach can detect cloud pixels over different periods and in different scenes successfully,thus promoting the use of HJ-1B data and improving the precision of image classification.

Keywords spectral feature decision tree classification remote sensing source region of the Yarlung Zangbo river grassland types

TP 751.1

Issue Date: 03 June 2012

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	SUN Ming
	SHEN Wei-shou
	XIE Min
	LI Hai-dong
	GAO Fei

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SUN Ming,SHEN Wei-shou,XIE Min, et al. Research on Algorithm of Cloud Detection for HJ-1B Image Based on Dynamical Thresholding[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 12-18.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.03 OR https://www.gtzyyg.com/EN/Y2012/V24/I2/12

[1] 易玲,汪潇,刘斌.HJ-1卫星数据质量及其在土地利用中的应用研究[J].国土资源遥感,2009(3):74-77.
[2] 孙中平,熊文成,魏斌,等.环境一号卫星CCD影像质量评价研究[J].红外,2010,31(9):30-36.
[3] 单娜,郑天垚,王贞松.快速高准确度云检测算法及其应用[J].遥感学报,2009,13(6):1147-1155.
[4] Rossow W B,Mosher F,Kinsella E,et al.ISCCP Cloud Algorithm Intercomparison [J].Journal of Climate and Applied Meteorology,1985,24(9):877-903.
[5] Kriebel K T,Gesell G,Kïstner M,et al.The Cloud Analysis Tool APOLLO:Improvements and Validations[J].International Journal of Remote Sensing,2003,24(12):2389-2048.
[6] Stowe L L,McClain E P,Carey R,et al.Global Distribution of Cloud Cover Derived from NOAA/AVHRR Operational Satellite Data[J].Adv Space Res,1991,11(3):51-54.
[7] 刘健.FY-2云检测中动态阈值提取技术改进方法研究[J].红外与毫米波学报,2010,29(4):288-292.
[8] 任平,杨存健,周介铭.HJ-1A/B星CCD多光谱遥感数据特征评价及应用研究[J].遥感技术与应用,2010,25(1):138-142.
[9] 何全军,曹静,黄江,等.基于多光谱综合的MODIS数据云检测研究[J].国土资源遥感,2006(3):19-22.
[10] 杨昌军,许健民,赵凤生.时间序列在FY2C云检测中的应用[J].大气与环境光学学报,2008,3(5):377-391.
[11] 郭洪涛,王毅,刘向培,等.卫星云图云检测的一种综合优化方法[J].解放军理工大学学报:自然科学版,2010,11(2):221-227.
[12] 刘显通,刘奇,傅云飞,等.基于TRMM VIRS可见光和红外五通道的白天云检测方案[J].大气与环境光学学报,2010,5(2):128-140.
[13] 宋小宁,赵英时.MODIS图象的云检测及分析[J].中国图象图形学报(A辑),2003,8(9):1079-1082.
[14] 李炳燮,马张宝,齐清文,等.Landsat TM遥感影像中厚云和阴影去除[J].遥感学报,2010,14(3):1-6.
[15] 巩慧.HJ-1星CCD相机在轨辐射定标与真实性检验研究[D].北京:中国科学院遥感应用研究所,2010.
[16] 葛永慧.测量平差[M].北京:中国矿业大学出版社,2005.
[17] 张从容,张正,张为良,等.利用卫星遥感进行海上透明云薄云的检测[J].海洋预报,2005,22(z1):87-93.

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