基于全变分模型的多时相遥感影像厚云去除算法

doi:10.11772/j.issn.1001-9081.2019111902

计算机应用 ›› 2020, Vol. 40 ›› Issue (7): 2126-2130.DOI: 10.11772/j.issn.1001-9081.2019111902

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于全变分模型的多时相遥感影像厚云去除算法

王睿, 黄微, 胡南强

上海大学通信与信息工程学院, 上海 200444

收稿日期:2019-11-08 修回日期:2019-12-13 出版日期:2020-07-10 发布日期:2019-12-26
通讯作者: 王睿
作者简介:王睿(1994-),男,安徽安庆人,硕士研究生,主要研究方向:遥感图像辐射校正与信息恢复;黄微(1980-),女,湖南汉寿人,讲师,博士,主要研究方向:遥感图像辐射校正与信息恢复;胡南强(1996-),男,上海人,主要研究方向:遥感图像辐射校正与信息恢复。

Thick cloud removal algorithm for multi-temporal remote sensing images based on total variation model

WANG Rui, HUANG Wei, HU Nanqiang

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2019-11-08 Revised:2019-12-13 Online:2020-07-10 Published:2019-12-26

摘要/Abstract

摘要： 针对多时相遥感影像厚云去除出现的亮度不一致和明显边界的问题，提出了一种结合全变分模型和泊松方程的多时相遥感影像厚云去除算法。首先，通过多时相遥感影像间共同区域的亮度信息计算亮度校正系数，对图像的亮度进行校正，降低亮度差异对去云结果的影响。然后，基于选择多源全变分模型对亮度校正后的多时相遥感影像进行重建，提高融合结果的空间平滑性及其与原始影像的相似性。最后，利用泊松方程对重建图像的局部区域进行优化。实验结果表明，该算法能够有效解决亮度不一致和边界问题。

关键词: 厚云去除, 全变分模型, 多时相遥感影像, 亮度校正, 泊松方程

Abstract: Brightness inconsistency and obvious boundary affect the reconstruction results of multi-temporal remote sensing images. In order to solve the problem, an improved thick cloud removal algorithm for multi-temporal remote sensing image was proposed by combining total variation model and Poisson equation. Firstly, the brightness correction coefficient was calculated by the brightness information of the common area of multi-temporal remote sensing images in order to correct the brightness of the images, so as to reduce the effect of brightness differences on cloud removal results. Then, multi-temporal images after brightness correction were reconstructed based on selective multi-source total variation model, and the fusion results' spatial smoothnesses and their similarities with the original images were improved. Finally, the local areas of the reconstruction image were optimized by using Poisson equation. The experimental results show that this method can effectively solve the problems of brightness inconsistency and boundary.

Key words: thick cloud removal, total variation model, multi-temporal remote sensing image, brightness correction, Poisson equation

中图分类号:

TP751

王睿, 黄微, 胡南强. 基于全变分模型的多时相遥感影像厚云去除算法[J]. 计算机应用, 2020, 40(7): 2126-2130.

WANG Rui, HUANG Wei, HU Nanqiang. Thick cloud removal algorithm for multi-temporal remote sensing images based on total variation model[J]. Journal of Computer Applications, 2020, 40(7): 2126-2130.

参考文献

[1] LIN C H,TSAN P H,LAI K H,et al. Cloud removal from multitemporal satellite images using information cloning[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):232-241.
[2] MAALOUF A,CARRE P,AUGEREAU B,et al. A Bandeletbased inpainting technique for clouds removal from remotely sensed images[J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(7):2363-2371.
[3] ZHU X,WU F,XIANG R,et al. Cloud removal for single images based on dual tree complex wavelet transform[C]//Proceedings of the 10th International Conference on Signal Processing and Communication Systems. Piscataway:IEEE,2016:1-6.
[4] 唐王琴, 梁栋, 胡根生, 等. 基于支持向量机的遥感图像厚云去除算法[J]. 遥感技术与应用, 2011, 26(1):111-116.(TANG W Q, LIANG D,HU G S,et al. The algorithm for removing thick clouds in remote sensing image based on support vector machine[J]. Remote Sensing Technology and Application, 2011, 26(1):111-116.)
[5] 梁栋, 孔颉, 胡根生, 等. 基于支持向量机的遥感影像厚云及云阴影去除[J]. 测绘学报, 2012, 41(2):225-231, 238.(LIANG D, KONG J,HU G S,et al. The removal of thick cloud and cloud shadow of remote sensing image based on support vector machine[J]. Acta Geodaetica et Cartographica Sinica,2012,41(2):225-231,238.)
[6] 赵孟银, 郑小慎, 刘文静. 基于相似像元替换的遥感影像厚云去除方法研究[J]. 计算机应用研究, 2016, 33(11):3509-3512. (ZHAO M Y,ZHENG X S,LIU W J. Study of thick clouds removal from remotely sensed images based on similar pixel substitution approach[J]. Application Research of Computers,2016,33(11):3509-3512.)
[7] XU M,JIA X,PICKERING M. Automatic cloud removal for Landsat 8 OLI images using cirrus band[C]//Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium. Piscataway:IEEE,2014:2511-2514.
[8] RAKWATIN P,TAKEUCHI W,YASUOKA Y. Restoration of aqua MODIS band 6 using histogram matching and local least squares fitting[J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(2):613-627.
[9] GLADKOVA I,GROSSBERG M D,SHAHRIAR F,et al. Quantitative restoration for MODIS band 6 on Aqua[J]. IEEE Transactions on Geoscience and Remote Sensing,2012,50(6):2409-2416.
[10] SALBERG A B. Land cover classification of cloud-contaminated multitemporal high-resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(1):377-387.
[11] 胡志勇, 杨学志, 董张玉, 等. 利用引导图像块排序的遥感影像厚云去除算法[J]. 遥感信息, 2018, 33(1):21-28.(HU Z Y, YANG X Z,DONG Z Y,et al. Cloud removal in remotely sensed image via smooth patch ordering guided with a multi-temporal image[J]. Remote Sensing Information,2018,33(1):21-28.)
[12] ZENG C,SHEN H,ZHANG L. Recovering missing pixels for Landsat ETM+SLC-off imagery using multi-temporal regression analysis and a regularization method[J]. Remote Sensing of Environment,2013,131:182-194.
[13] CHENG Q,SHEN H,ZHANG L,et al. Cloud removal for remotely sensed images by similar pixel replacement guided with a spatiotemporal MRF model[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2014,92:54-68.
[14] TIERNEY S,GUO Y,GAO J. Selective multi-source total variation image restoration[C]//Proceedings of the 2015 International Conference on Digital Image Computing:Techniques and Applications. Piscataway:IEEE,2015:1-8.
[15] 蒋正金, 端木春江. 卡通纹理分解和全变分梯度算法实现图像恢复[J]. 计算机工程与应用, 2014, 50(2):162-169.(JIANG Z J,DUANMU C J. Using cartoon texture image decomposition and GAPG for total-variation-based image restoration[J]. Computer Engineering and Applications,2014,50(2):162-169.)
[16] ZHU Z,WOODCOCK C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment,2012,118:83-94.
[17] PÉREZ P,GANGNET M,BLAKE A. Poisson image editing[J]. ACM Transactions on Graphics,2003,22(3):313-318.
[18] BHANDARI A K,KUMAR A,SINGH G K. Improved knee transfer function and gamma correction based method for contrast and brightness enhancement of satellite image[J]. AEU-International Journal of Electronics and Communications,2015,69(2):579-589.

基于全变分模型的多时相遥感影像厚云去除算法

Thick cloud removal algorithm for multi-temporal remote sensing images based on total variation model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

[1]	熊兴隆, 杨立香, 马愈昭, 庄子波. 基于模糊C均值的低空风切变预警算法[J]. 计算机应用, 2018, 38(3): 655-660.
[2]	黄微傅利琴王琛. 基于梯度域的保纹理图像阴影去除算法[J]. 计算机应用, 2013, 33(08): 2317-2319.
[3]	胡文瑾李战明. 基于全变分小波域缺损修复算法的一种数值实现[J]. 计算机应用, 2013, 33(01): 28-30.
[4]	车辚辚孔英会. 基于动态部位特征的步态识别方法[J]. 计算机应用, 2012, 32(12): 3418-3421.
[5]	蒋伟. 基于分数阶偏微分方程的图像去噪新模型[J]. 计算机应用, 2011, 31(03): 753-756.
[6]	周艳霞秦开怀罗建利. 多投影仪组合显示的自反馈亮度校正[J]. 计算机应用, 2011, 31(01): 65-69.
[7]	王益艳. 基于广义变分模型的自适应图像去噪算法[J]. 计算机应用, 2009, 29(11): 3033-3036.