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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (s1) : 34-38     DOI: 10.6046/gtzyyg.2017.s1.06
Orginal Article |
Destriping model of GF-2 image based on moment matching
CUI Jian1, SHI Penghui1, BAI Weiming2, LIU Xiaojing1
1. Henan Institute of Geological Survey, Zhengzhou 450001, China;
2. Henan Aero Geophysical Survey and Remote Sensing Center, Zhengzhou 450001, China
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Abstract  Gaofen 2(GF-2) is China’s first civil optical remote sensing satellite with spatial resolution better than 1 m. It has five bands, with wave length range from visible to near infrared light and spatial resolution under star as precise as 0.8 m. Randomly streaking noise was found in the work, which affected interpretation and information extraction. According to the features of GF-2 image stripe, the destriping of GF-2 image was carried out by using moment matching. Then, the destriping result was analyzed by qualitative or quantitative analysis methods. The results show that moment matching can effectively eliminate the streaking noise of the GF-2.
Keywords heuristics      optimization      agricultural area      high resolution remote sensing image(HRI)     
Issue Date: 24 November 2017
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SU Tengfei
ZHANG Shengwei
LI Hongyu
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SU Tengfei,ZHANG Shengwei,LI Hongyu. Destriping model of GF-2 image based on moment matching[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 34-38.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.s1.06     OR     https://www.gtzyyg.com/EN/Y2017/V29/Is1/34
[1] Ahern F J,Brwon R J,Cihlar J,et al.Radiometric correction of visible and infrared remote sensing data at the Canada Centre for Remote Sening[J].International Joumal of Remote Sensing,1987,8(9):1349-1376.
[2] Crippen R E.A simple spatial filtering routine for the cosmetric removal of scan-line noise from Landsat TM P-tape imagery[J].Photogram-metric Engineering and Remote Sensing,1989,55(3):327-331.
[3] Gadallah F L,Csillag F,Smith E J M.Destriping multisensor imagery with moment matching[J].International Joumal of Remote Sensing,2000,21(12):2505-2511.
[4] Sun L,Neville R,Staenz K,et al.Automatic destriping of Hyperion imagery based on spectral moment matching[J].Canadian Journal of Remote Sensing,2008,34(S1):S68-S81.
[5] 牛生丽,唐军武,蒋兴伟,等.HY-1A卫星COCTS数据条带消除的两种定量化方法比较[J].遥感学报,2007,11(6):327-331.
Niu S L,Tang J W,Jiang X W,et al.The comparison of two quantitative striping removal algorithms for HY-1A COCTS data[J].Journal of Remote Sensing,2007,11(6):327-331.
[6] 修吉宏,翟林培,刘 红.CCD图像条带噪声消除方法[J].电子器件,2005,28(4):719-721.
Xiu J H,Zhai L P,Liu H.Method of removing striping noise in CCD image[J].Chinese Journal of Electron Devices,2005,28(4):719-721.
[7] 秦 雁,邓孺孺,何颖清,等.分段线性动态矩匹配条带去除[J].中国图象图形学报,2012,17(11):1444-1452.
Qin Y,Deng R R,He Y Q,et al.Piece-wise linear dynamic moment matching destriping[J].Journal of Image and Graphics,2012,17(11):1444-1452.
[8] 贺 霖,潘 泉,邸 韡,等.一种基于单似然检验的高光谱图像小目标检测器[J].光学学报,2007,27(12):2155-2162.
He L,Pan Q,Di W,et al.A small-target detector based on single likelihood test for hyperspectral imagery[J].Acta Optica Sinica,2007,27(12):2155-2162.
[9] Wegener M.Destriping multiple sensor imagery by improved histogram matching[J].International Journal of Remote Sensing,1990,11(5):859-875.
[10] 陈雪洋,袁 超.ZY-1 02C卫星影像融合方法评价[J].测绘与空间地理信息,2013,36(2):50-53.
Chen X Y,Yuan C.Data fusion evaluation of ZY-1 02C satellite images[J].Geomatics and Spatial Information Technology,2013,36(2):50-53.
[11] Chavez Jr P S,Sides S C,Anderson J A.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic[J].Photogrammetric Engineering and Remote Sensing,1991,57(3):295-303.
[12] 崔 璨.基于对比分析的国产卫星影像质量评价[D].长春:吉林大学,2014.
Cui C.The Quality Assessment of Domestic Satellite Images Based on Contrastive Analysis[D].Changchun:Jilin University,2014.
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