Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2011, Vol. 23 Issue (4) : 132-135     DOI: 10.6046/gtzyyg.2011.04.24
Technology Application |
Research on the Thin Cloud Removal Method from RapidEye Image in Western Mountain Areas
YIN Feng1, XIONG De-ke2, XIE Fei2
1. Land Surveying & Planning Institute of Hubei Province, Wuhan 430000, China;
2. Land Surveying and Mapping Institute of Hubei Province, Wuhan 430010, China
Download: PDF(2589 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Two methods for removing the thin cloud cover from the RapidEye remote sensing imagery in western mountain areas of China based on Photoshop environment are introduced in this paper. One is to directly adjust the three bands respectively by adjustment function of levels, and the other is band substitution. In the second method, the first step is to substitute infrared band for blue band, and then three bands are adjusted respectively in levels. Test results show that the imagery quality is enhanced obviously when the processed imagery is compared with the original one, and the efficiency in the production practice is improved.

Keywords Smallest Univalue Segment Assimilating Nucleus (SUSAN)      Watershed Transform (WT)      QuickBird imagery      Image segmentation     
:  751.1  
Issue Date: 16 December 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
XUE Qiao
ZHAO Shu-he
Cite this article:   
XUE Qiao,ZHAO Shu-he. Research on the Thin Cloud Removal Method from RapidEye Image in Western Mountain Areas[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(4): 132-135.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.04.24     OR     https://www.gtzyyg.com/EN/Y2011/V23/I4/132



[1] 曹爽,李浩,马文.基于数学形态学的遥感影像薄云处理方法[J].地理与地理信息科学2009,25(4):30-33.



[2] 祝振江,周英杰,周萍,等.RapidEye卫星遥感影像几何精度的实验分析[J].中南林业科技大学学报,2010,30(4):107-111.



[3] 同天视地.RapidEye卫星产品简介[EB/OL].[2011-02- 25].http://www.bjeo.com.cn/pubnews/213730/20090218/215808.jsp.



[4] 党安荣,王晓栋,陈晓峰,等.ERDAS IMAGINE遥感图像处理方法[M].北京:清华大学出版社,2003.



[5] 贺辉,彭望琭,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):306-311.



[6] 李超,朱满,赵家平.多源遥感影像融合效果的定量评价研[J].测绘与空间地理信息,2010,33(3):143-146.



[7] 刘洋,白俊武.遥感影像中薄云的去除方法研究[J].测绘与空间地理信息,2008,31(3):120-122.

[1] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2] 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.
[3] Biqing WANG, Wenquan HAN, Chi XU. Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 219-225.
[4] Shicai ZHU, Xiaotong ZHAI, Zongwei WANG. Segmentation of large scale remote sensing image based on Mean Shift[J]. Remote Sensing for Land & Resources, 2020, 32(1): 13-18.
[5] Peiqing LOU, Xiaoyu CHEN, Shutong WANG, Bolin FU, Yongyi HUANG, Tingyuan TANG, Ming LING. Object recognition of karst farming area based on UAV image: A case study of Guilin[J]. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
[6] Dechao ZHAI, Yanan FAN, Yanan ZHOU. Multi-scale segmentation of satellite imagery by edge-incorporated weighted aggregation[J]. Remote Sensing for Land & Resources, 2019, 31(3): 36-42.
[7] Bingxiu YAO, Liang HUANG, Yansong XU. A high resolution remote sensing image segmentation method based on superpixel and graph theory[J]. Remote Sensing for Land & Resources, 2019, 31(3): 72-79.
[8] Yongmei ZHANG, Haiyan SUN, Yulong XU. An improved multispectral image segmentation method based on super-pixels[J]. Remote Sensing for Land & Resources, 2019, 31(1): 58-64.
[9] Jun YANG, Jianjie PEI. An improved ICM algorithm for remote sensing image segmentation[J]. Remote Sensing for Land & Resources, 2018, 30(3): 18-25.
[10] Liang LI, Lei WANG, Kai WANG, Sheng LI. A change detection method for vector map and remote sensing imagery based on object heterogeneity[J]. Remote Sensing for Land & Resources, 2018, 30(1): 30-36.
[11] ZHAO Qingping. Wide-swath SAR ice images segmentation based on Lambert’s law[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 67-71.
[12] ZHU Hongchun, HUANG Wei, LIU Haiying, ZHANG Zhongfang, WANG Bin. Research on object-oriented remote sensing change detection method based on KL divergence[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 46-52.
[13] SU Tengfei, ZHANG Shengwei, LI Hongyu. Segmentation algorithm based on texture feature and region growing for high-resolution remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 72-81.
[14] ZHANG Tao, YANG Xiaomei, TONG Liqiang, HE Peng. Selection of best-fitting scale parameters in image segmentation based on multiscale segmentation image database[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 59-63.
[15] LI Liang, LIANG Bin, XUE Peng, YING Guowei. Remote sensing image segmentation under vector map constraints[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 80-85.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech