Please wait a minute...
img

官方微信

遥感技术与应用  2016, Vol. 31 Issue (3): 511-517    DOI: 10.11873/j.issn.1004-0323.2016.3.0511
数据与图像处理     
基于加权梯度融合的Landsat影像薄云去除
黄微,张婷婷,聂龙保
(上海大学通信与信息工程学院,上海 200444)
A Weighted Gradient\|based Fusion Method for Thin Cloud Removal of Landsat Images
Huang Wei,Zhang Tingting,Nie Longbao
(School of Communication and Information Engineering,Shanghai 200444,China)
 全文: PDF(19703 KB)  
摘要:

遥感影像中薄云的存在为影像的判读带来了极大的影响,通过研究薄云对Landsat影像造成的影响,提出一种加权梯度融合变分模型.通过在无云区域采用较小权重以保持影像自身信息,薄云区域则采用较大权重将参考影像的梯度信息融入待修复影像,改进了梯度模型在无云区域过度增强细节而造成的失真.采用暗通道法和梯度融合法与该方法进行比较,实验结果表明:该方法在有效去除薄云的同时对无云区域有较好的保真效果.

关键词: 薄云去除梯度融合变分模型加权    
Abstract:

The presence of thin cloud in remote sensing images has brought great impact for follow\|up image interpretation.To remove thin cloud of Landsat images,a weighted gradient\|based total variation fusion method is proposed.Considering the highly correlation and complementarity between the infrared and visible bands,we choose gradient information of infrared bands as reference.In our method,lower weights are used in cloudless areas to integrate the gradient information of reference images into the restored images.By doing so,the spectrum information of cloudless areas is well kept.Oppositely,higher weights in thin cloud areas are used to remove cloud.In contrast with dark channel method and gradient\|based method,our method is effective visually to remove thin cloud and modify spectral distortion causing by excessive detail enhancement of gradient\|based model.Quantitatively,the difference index R of our method in the cloudless areas is significantly lower than that of other methods.

Key words: Thin cloud removal;Gradient\    based fusion;Total variation model;Weighted
收稿日期: 2015-09-08 出版日期: 2016-07-19
:  TP79   
作者简介: 黄微(1980-),女,湖南汉寿人,讲师,主要从事遥感图像辐射校正与信息恢复研究.Email:lyxhw@shu.edu.cn.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
黄微
张婷婷
聂龙保

引用本文:

黄微,张婷婷,聂龙保. 基于加权梯度融合的Landsat影像薄云去除[J]. 遥感技术与应用, 2016, 31(3): 511-517.

Huang Wei,Zhang Tingting,Nie Longbao. A Weighted Gradient\|based Fusion Method for Thin Cloud Removal of Landsat Images. Remote Sensing Technology and Application, 2016, 31(3): 511-517.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.3.0511        http://www.rsta.ac.cn/CN/Y2016/V31/I3/511

[1]Richter R,Wang X,Bachmann M.Correction of Cirrus Effects in Sentinel-2 Type of Imagery[J].Remote Sensing,2011,32(10):2931-2941.

[2]Du Y,Guindona B,Cihlara J.Haze Detection and Removal in High Resolution Satellite Image with Wavelet Analysis[J].IEEE Transaction on Geoscience and Remote Sensing,2001,40(1):210-217.

[3]Stockham T G.Image Processing in the Context of a Visual Model[J]. Proceedings of The IEEE,1972,60(7):828-842.

[4]Feng Chun,Ma Jianwen,Dai Qin,et al.An Improved Method for Rapid Removal of Thin Cloud in Remote Sensing Images[J].Remote Sensing for Land and Resources,2004,(4):1-3.[冯春,马建文,戴秦,等.一种改进的遥感图像薄云快速取出方法[J].国土遥感资源,2004,(4):1-3.]

[5]Zhou Xiaojun,Guo Jia,Zhou Chengxian,et al.An Algorithm of Cloud Remocal for Remote Sensing Image based on Improved Homomorphic Filtering[J].Radio Engineering,2015(3):14-18[周小军,郭佳,周承仙,等.基于改进同态滤波的遥感图像去云算法[J].无线电工程,2015(3):14-18.]

[6]Wu X P,Yang W N,Li G M.Thin Cloud Removal of ZY-3 Image based on Improved Homomorphism Filtering Method.International Conference on Geoinformatics[C]//21st International Conference on Geoinfornation,2013:1-4.

[7]Shen H,Li H,Qian Y,et al.An Effective Thin Cloud Removal Procedure for Visible Remote Sensing Images[J].SPRS Journal of Photogrammetry and Remote Sensing,2014,96(11):224-235.

[8]Chander G,Helder D L,Boncyk W C.Landsat-4 /5 Band 6 Relative Radiometry[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(1):206-210.

[9]Garcla J C,Moreno J.Removal of Noises in Chris/Proba Images:Application to Space Campaign Data[C]//Proceedings of the 2nd Chris/Proba Workshop,2004:28-30.

[10]Li Hongli,Shen Huanfeng,Du Bo,et al.A High-fidelity Method of Removing Thin Cloud from Remote Sensing Digital Images based on Homomorphic Filtering[J].Remote Sensing Application,2011,(1):41-44.[李洪利,沈焕峰,杜博,等.一种高保真同态滤波遥感影像薄云去除方法[J].遥感信息,2011,(1):41-44.]

[11]Chavez J.An Improved Dark-object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data[J].Remote Sensing of Environment,1988,24(3):459-479.

[12]Zhang Y,Guindona B,Cihlara J.An Image Transform to Characterize and Compensate for Spatial Variations in Thin Cloud Contamination of Landsat Images[J].Remote Sensing of Environment,2002,82(2-3):173-187.

[13]He X Y,Hu J B,Chen W.Haze Removal based on Advanced Haze-optimized Transformation (AHOT) for Multispectral Imagery[J].International Journal of Remote Sensing,2010,31(20):5331-5348.

[14]Lan X,Zhang L,Shen H.Single Image Haze Removal Considering Sensor Blur and Noise[J].EURASIP Journal on Advances in Signal Processing,2013,(1):1-13.

[15]Zhang Y,Guindon B.Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery:Effect on Land Cover Classification Performance[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(5):1082-1089.

[16]Lü H T.Removal of Thin Clouds in Visible Bands Using Spectrum Characteristics of the Visible Bands[C]//IEEE International Geoscience and Remote Sensing Symposium,2015:929-932.

[17]Liu C B,Hu J B.Haze Detection,Perfection and Removal for High Spatial Resolution Satellite Imagery[J].International Journal of Remote Sensing,2010,32(23):8685-8697.

[18]Li H,Zhang L,Shen H,Li P.A Variational Gradient-based Fusion Method for Visible and SWIR Images[J].Photogrammetric Engineering and Remote Sensing,2012,78(9):947-958.

[19]Kaufman Y J,Sendra C.Algorithm for Automatic Atmospheric Corrections to Visible and Near-IR Satellite Imagery[J].International Journal of Remote Sensing,1988,9(8):1357-1381.

[20]Karnieli A,Kaufman Y J,Remer L.AFRI—Aerosol Free Vegetation Index[J].Remote Sensing of Environment,2001,77(1):10-21.

[21]He K M,Sun J,Tang X D.Guided Image Filtering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397 -1409.

[22]Zhu X,Milanfar P.Automatic Parameter Selection for Denoising Algorithms Using a No-reference Measure of Image Content[J].IEEE Transactions on Image Processing,2010,19(12):3116-3132.

[1] 王生明,李永树,何敬. 一种改进的局部加权拟合校正方法[J]. 遥感技术与应用, 2013, 28(1): 85-89.
[2] 靳志宾,蒲英霞,陈刚,王结臣,马劲松,杨萌萌. 基于地理加权的k-|NN高分辨率遥感影像分类算法改进[J]. 遥感技术与应用, 2013, 28(1): 97-102.
[3] 陈芸芝,汪小钦,吴波,孙丽雅. 基于自适应加权平均的水色遥感数据融合[J]. 遥感技术与应用, 2012, 27(3): 333-338.
[4] 郭振亚,王心源,王传辉,高 超,吴海中. 巢湖流域水体信息提取方法研究[J]. 遥感技术与应用, 2012, 27(3): 443-448.
[5] 张 婷,许 可. 一种提高海洋雷达高度计重跟踪估值精度的处理方法[J]. 遥感技术与应用, 2007, 22(3): 422-427.
[6] 唐国栋,柯长青. 中国西部地区积雪深度的空间插值比较[J]. 遥感技术与应用, 2007, 22(1): 39-44.
[7] 任红玲, 晏明, 涂刚. NOAA/AVHRR观测角影响纠正的一种方法[J]. 遥感技术与应用, 1999, 14(2): 34-38.