高空间分辨率卫星图像的薄云去除

大气与环境光学学报 ›› 2018, Vol. 13 ›› Issue (4): 285-292.

高空间分辨率卫星图像的薄云去除

王晴1,2，崔生成 1，杨世植1

（1中国科学院安徽光学精密机械研究所中国科学院大气成分与光学重点实验室，安徽合肥230031;
2中国科学技术大学研究生院科学岛分院，安徽合肥230026）

收稿日期:2017-04-21 修回日期:2017-06-01 出版日期:2018-07-28 发布日期:2018-07-10
通讯作者: 王晴(1992-)，女，硕士，主要从事光学遥感技术方面的研究. E-mail:18715103609@163.com
作者简介:王晴(1992-)，女，硕士，主要从事光学遥感技术方面的研究.
基金资助:
Supported by National Nature Science Foundation of China(国家自然科学基金, 41305019)

Thin Cloud Removal for High Spatial Resolution Satellite Images

WANG Qing1,2, CUI Shengcheng1, YANG Shizhi1

(1 Key Laboratory of Atmospheric Composition and Radiation, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China;
2 University of Science and Technology of China, Hefei 230026, China)

Received:2017-04-21 Revised:2017-06-01 Published:2018-07-28 Online:2018-07-10
Contact: 王晴(1992-)，女，硕士，主要从事光学遥感技术方面的研究. E-mail:18715103609@163.com
About author:王晴(1992-)，女，硕士，主要从事光学遥感技术方面的研究.
Supported by:
Supported by National Nature Science Foundation of China(国家自然科学基金, 41305019)

摘要/Abstract

摘要：

为了有效地去除高分辨卫星图像中薄云的影响，提出了一种基于Mallat小波变换的薄云去除方法。对图像进行Mallat小波分解得到高频细节部分和低频近似部分，根据云噪声在分解系数中处于低频部分而地物信息占据相对高频部分的特点，在小波变换多尺度分析的基础上，算法在最大尺度低频图像上按照云厚度掩膜值对云区进行线性处理；对于高频子带图像根据尺度的不同运用非线性增强算子进行不同程度的增强，从而提高图像的清晰度，减小残留云的影响，之后将重构后的图像进行中值滤波以减少高频云的影响。针对高分一号卫星图像进行了实验。实验证明，该方法在去除薄云的同时很好地保留了图像细节及边缘信息，去薄云效果优于传统小波变换法。

关键词: 卫星图像, 薄云, Mallat算法, 多尺度分析, 非线性增强, 中值滤波

Abstract:

In order to remove the influence of thin cloud on satellite image effectively, an algorithm based on Mallat wavelet transform is proposed. It’s it possible to decompose the image into high frequency detail and low frequency approximation components. Based on the facts that cloud noise occupies lower frequency part in the distribution characteristics while scenery information make up the relative high part, this kind of algorithm processes cloudy zones by using linear methods according to cloud thickness in the largest scale of low frequency sub-band image. Different scales of high frequency sub-band is enhanced by non-linear enhancing operators in order to improve images’ sharpness and reduce the impact of residual cloud. Later median filtering is added to process the reconstruct image to reduce the influence of high frequency mutation cloud. The algorithm is utilized to process GF-1 images. Experiment shows that this kind of algorithm can remove thin cloud while it can also preserve image details and edges the same time, which indicates it is better than traditional wavelet transform method.

Key words: satellite image, thin cloud; Mallat algorithm, multi-scale analysis, non-linear enhancement, median filtering

中图分类号:

TP751

王晴崔生成杨世植. 高空间分辨率卫星图像的薄云去除[J]. 大气与环境光学学报, 2018, 13(4): 285-292.

WANG Qing, CUI Sheng-Cheng, YANG Shi-Zhi. Thin Cloud Removal for High Spatial Resolution Satellite Images[J]. Journal of Atmospheric and Environmental Optics, 2018, 13(4): 285-292.

参考文献

[1] Xu Jiayao. Haze Removal Research for GF-1 Satellite Image [D]. Beijing: Master’s Thesis of China University of Geosciences, 2015(in Chinese).
许佳垚. 高分一号卫星影像去薄云方法研究 [D]. 北京: 中国地质大学硕士论文, 2015.
[2] Li Haiwei. Research on Removing Thin Cloud in Single Remote Sensing Image [D]. Changsha: Master’s Thesis of Central South University, 2012(in Chinese).
李海巍. 单幅遥感影像去薄云方法研究 [D]. 长沙: 中南大学硕士论文, 2012.
[3] Richter R. Atmospheric correction of satellite data with haze removal including a haze/clear transition region [J]. Computers & Geosciences, 1996, 22(6): 675-681.
[4] Xie Huamei, He Qi’ao, Zheng Ning, et al. The improved homomorphic fileter algorithm for removing cloud of remote sensing image based on the second exploiture of ERDAS tool [J]. Jounal of Beijing Normal University: Natural Science, 2005, 41(2): 150-153(in Chinese).
谢华美, 何启翱, 郑宁, 等. 基于ERDAS二次开发的遥感图像同态滤波薄云去除算法的改进 [J]. 北京师范大学学报: 自然科学版, 2005, 41(2): 150-153.
[5] Shen Wenshui, Zhou Xinzhi. Algorithm for removing thin cloud from remote sensing digital images based on homomorphic filtering [J]. High Power Laser and Particle Beams, 2010, 22(1): 45-48(in Chinese).
沈文水, 周新志. 基于同态滤波的遥感薄云去除算法 [J]. 强激光与粒子束, 2010, 22(1): 45-48.
[6] Zhu Xifang, Wu Feng, Tao Chunkan. A new algorithm of cloud removing for optical images based on wavelet threshold theory [J]. Acta Photonica Sinica, 2009, 38(12): 3312-3317(in Chinese).
朱锡芳, 吴峰, 陶纯堪. 基于小波阈值理论的光学图像去云处理新算法 [J]. 光子学报, 2009, 38(12): 3312-3317.
[7] Chen Fen, Yan Dongmei, Zhao Zhongming. Haze detection and removal in remote sensing images based on undecimated wavelet transform [J]. Geomatics and Information Science of Wuhan University, 2007, 32(1): 71-74(in Chinese).
陈奋, 闫冬梅, 赵忠明. 基于无抽样小波的遥感影像薄云检测与去除 [J]. 武汉大学学报: 信息科学版, 2007, 32(1): 71-74.
[8] Wang Hengjin. Research and Implementation of Remote Sensing Image Thin Cloud Removal Based on Wavelet [D]. Xi’an: Master’s Thesis of Northwestern Polytechnical University, 2002(in Chinese).
王恒进. 基于小波的遥感图像薄云去除的研究与实现 [D]. 西安: 西北工业大学硕士论文, 2002.
[9] Yang Wenliang. Research on Removing Thin Cloud in Single High-Resolution Remote Sensing Image [D]. Changsha: Master’s Thesis of Central South University, 2010.
杨文亮. 单景高分辨率遥感影像薄云去除研究 [D]. 长沙: 中南大学硕士论文, 2011.
[10] Cheng Lizhi. Wavelet Analysis [M]. Beijing: Science Press, 2004(in Chinese).
成礼智. 小波的理论与应用 [M]. 北京: 科学出版社, 2004.
[11] Wu Yinqian, Shi Pengfei. Approach on image contrast enhancement based on wavelet transform [J]. Infrared and Laser Engineering, 2003, 32(1): 4-7(in Chinese).
吴颖谦, 施鹏飞. 基于小波变换的低对比度图像增强 [J]. 红外与激光工程, 2003, 32(1): 4-7.
[12] Gonzalez R, Woods R. Digital Image Processing [M]. Addison-Wesley, 2010.
[13] Lu X, Liu R, Liu J, et al. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products [J]. Photogrammetric Engineering & Remote Sensing, 2007, 73(10): 1129-1139.
[14] Wang Hongnan, Zhong Wen, Wang Jing, et al. Research of measurement for digital image definition [J]. Journal of Image and Graphics, 2004, 9(7): 828-831(in Chinese).
王鸿南，钟文，王静，等.图像清晰度评价方法研究 [J]. 中国图像图形学报，2004，9(7)：828-831.