Comparison of the Methods for Haze Elimination of GeoEye-1 Remote Sensing Image

doi:10.6046/gtzyyg.2012.03.10

Abstract
References
Related Articles
Metrics

Download: PDF(1029 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract In the rainy and foggy areas,the images acquired by GeoEye-1 satellite are often affected by haze. Because of the influence of the absorption,refraction,reflection and scattering role of the haze on the electromagnetic waves,the image clarity is decreased. Therefore,it is necessary to eliminate the impact of the haze on the image as much as possible so as to effectively improve the quality of images. Using ENVI software as an image processing platform,the authors selected the "homomorphic filtering" and "wavelet transform" methods commonly used in China and abroad and the "band calculation composition" method proposed in this paper to make a comparative study of the effects of haze elimination for the GeoEye-1 image. The results show that the use of "band calculation composition" to remove the haze from GeoEye-1 image has a good effect.

Keywords classification land-use decision-tree

TP751.1

Issue Date: 20 August 2012

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	YANG Xi-guang
	HUANG Hai-jun
	YAN Li-wen
	DU Ben-xu

Cite this article:

YANG Xi-guang,HUANG Hai-jun,YAN Li-wen, et al. Comparison of the Methods for Haze Elimination of GeoEye-1 Remote Sensing Image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 50-53.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.03.10 OR https://www.gtzyyg.com/EN/Y2012/V24/I3/50

[1] 秦梓勤,丁明跃,张天序.雨雾天气下的户外场景成像[J].电子学报,2002,28(3):131-133. Qin Z Q,Ding M Y,Zhang T X.Imaging for Outdoor Scene in Rain and Fog[J].Acta Electronica Sinica,2002,28(3):131-133(in Chinese with English Abstract).
[2] 叶秋果,宗景春,李钏,等.基于同态滤波的遥感影像去云雾处理[J].海洋测绘,2009,29(3):45-47. Ye Q G,Zong J C,Li C,et al.Removing Cloud and Mist from Remote Sensing Digital Images Based on Homo Graphic Filtering[J].Hydrographic Surveying and Charting,2009,29(3):45-47(in Chinese with English Abstract).
[3] 杨必武,郭晓松,王克军,等.基于直方图非线性拉伸的红外图像新增强算法[J].红外与激光工程,2003,32(1):1-7. Yang B W,Guo X S,Wang K J,et al.New Algorithm of Infrared Image Enhancement Based on Histogram Nonlinear Extension[J].Infrared and Laser Engineering,2003,32(1):1-7(in Chinese with English Abstract).
[4] 殷年.基于线性拉伸的遥感图像处理[J].光电子技术与信息,1998(5):29-33. Ying N.Image Processing Based on Linear Extension of Gray Level[J].Optoelectronic Technology & Information,1998(5):29-33(in Chinese with English Abstract).
[5] 祝培,朱虹,钱学明,等.一种有雾天气图像景物影像的清晰化方法[J].中国图像学报A辑,2004(1):124-128. Zhu P,Zhu H,Qian X M,et al.An Image Clearness Method for Fog[J].Journal of Image and Graphics,2004,9(1):124-128(in Chinese with English Abstract).
[6] 祝培,朱虹,钱学明,等.一种有雾天气图像景物影像的清晰化方法[J].中国图像学报,2009(1):124-128. Zhu P,Zhu H,Qian X M,et al.An Image Clearness Method for Fog[J].Journal of Image,2009(1):124-128(in Chinese with English Abstract).
[7] 芮义斌,李鹏,孙锦涛,等.一种交互式图像去雾方法[J].计算机应用,2006,26(11):2733-2735. Rui Y B,Li P,Sun J T,et al.Interactive Defogging Method for Image[J].Journal of Computer Applications,2006,26(11):2733-2735(in Chinese with English Abstract).
[8] 贺辉,彭望瑜,匡锦瑜.自适应滤波的高分辨率遥感影像薄云去除算法[J].地球信息科学学报,2009,11(3):87-90. He H,Peng W Y, Kuang J Y.Cloud Cover Removed from High-resolution Remote Sensing Images Based on the Adaptive Filtering and Gray-scale Transformation[J].Journal of Geo-information Science,2009,11(3):87-90(in Chinese with English Abstract).
[9] 朱锡芳,徐芳芳.Retinex和小波变换去除遥感图像云雾方法分析[J].常州工学院学报,2007(5):8-13. Zhu X F, Xu F F.Analysis of the Methods to Remove Cloud from Remote Sensing Images Based on Retinex and Wavelet Theory[J].Journal of Changzhou Institute of Technology,2007(5):8-13(in Chinese with English Abstract).
[10] 燕文浩,马彩文,张鸣,等.基于小波变换的图像融合新算法[J].光子学报,2006,35(4):638-640. Yan W H,Ma C W,Zhang M,et al.A New Way for Image Fusion Based on Wavelet Transform[J].Acta Photonica Sinica,2006,35(4):638-640(in Chinese with English Abstract).
[11] 尹峰,熊德科,谢菲,等.西部山区RapidEye图像去薄云方法研究[J].国土资源遥感,2011(4):132-135. Yin F,Xiong D K,Xie F,et al.Research on the Thin Cloud Removal Method from RapidEye Image in Western Mountain Areas[J].Remote Sensing for Land and Resources,2011(4):132-135 (in Chinese with English Abstract).

[1]	SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[2]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[3]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[4]	FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[5]	WANG Juanjuan, WU Zhaopeng, WANG Shanshan, YIN Huihui. An analysis of the pattern of land-use conflicts in valley oases in arid areas[J]. Remote Sensing for Natural Resources, 2021, 33(4): 243-251.
[6]	WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[7]	JIANG Xiao, ZHONG Chang, LIAN Zheng, WU Liangting, SHAO Zhitao. Research progress on classification criterion of geological information products based on satellite remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 279-283.
[8]	BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[9]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[10]	LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[11]	MENG Qing, BAI Hongying, ZHAO Ting, GUO Shaozhuang, QI Guizeng. The eco-barrier effect of Qinling Mountain on aerosols[J]. Remote Sensing for Land & Resources, 2021, 33(1): 240-248.
[12]	XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[13]	ZHANG Li, XIE Yanan, QU Chenyang, WANG Mingquan, CHANG Zheng, WANG Maohua. Estimation of electric power consumption using nighttime light remote sensing data based on K-Means city classification algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 182-189.
[14]	WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[15]	WANG Yuefeng, WU Huizhi, HE Shujun, HUANG Di, BAI Chaojun. Method research of intelligentized extraction of natural resources information from Shihe District,Xinyang City,Henan Province[J]. Remote Sensing for Land & Resources, 2020, 32(4): 244-250.

Viewed

Full text

Abstract

Cited

Shared

Discussed