Analysis of correlation jam in the remote sensing image based on the transformed space Hough algorithm

GUO Qiang; CHEN Gui-lin; TONG Wei-qi

doi:null

您当前的位置：

首页 >

文章列表页 >

Analysis of correlation jam in the remote sensing image based on the transformed space Hough algorithm

更新时间：2020-08-12

- Analysis of correlation jam in the remote sensing image based on the transformed space Hough algorithm
- Optics and Precision Engineering Vol. 9, Issue 2, Pages: 121-126(2001)
- 作者机构：
  
  中国科学院上海技术物理研究所上海,200083
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP751
- Received：19 October 2000，
  
  Revised：10 November 2000，
  
  Published Online：15 April 2001，
  
  Published：15 April 2001
- 稿件说明：
移动端阅览
郭强, 陈桂林, 童卫旗. 基于变换域Hough变换的遥感图像相干干扰分析[J]. 光学精密工程, 2001,(2): 121-126

GUO Qiang, CHEN Gui-lin, TONG Wei-qi . Analysis of correlation jam in the remote sensing image based on the transformed space Hough algorithm[J]. Editorial Office of Optics and Precision Engineering, 2001,(2): 121-126
郭强, 陈桂林, 童卫旗. 基于变换域Hough变换的遥感图像相干干扰分析[J]. 光学精密工程, 2001,(2): 121-126 DOI：

GUO Qiang, CHEN Gui-lin, TONG Wei-qi . Analysis of correlation jam in the remote sensing image based on the transformed space Hough algorithm[J]. Editorial Office of Optics and Precision Engineering, 2001,(2): 121-126 DOI：

摘要

相干干扰的存在严重地影响了对遥感图像中目标的特征提取和识别的精度

降低了各种遥感定量分析方法的有效性。通过对相干干扰的幅度和相位特性的定量分析

本文提出了相干干扰的小视场模型和大视场模型

并给出了基于变换域的Hough变换算法。实践表明

与传统的邻域平均、中值滤波、经典频域滤波等消除干扰算法相比

该算法不仅高质量地消除了遥感图像中的相干干扰

同时还能有效地保留原图像中的细微影纹和边缘信息

并获得较好的峰值信噪比。该方法已在航天遥感图像的实时采集及处理系统中获得成功应用。

Abstract

Since the correlation jam has a serious influence on the feature extraction of remote sensing images

as well as on the precise identification of the remote-sensing ground-objects

the paper not only presents small-and large-visual angle models

but also gives corresponding transform space Hough algorithm on the basis of the quantitive analysis of the amplitude and the phase. Compared with the methods of neighbour average

median filtering and classical frequency space filtering

the algorithm proposed in the paper is useful in removing the random-phase jam of the images

reserving richer fine textures and edge information and getting higher PSNRs. The algorithm has been applied successfully to the real time aquisition and processing system for remote sensing images.

关键词

Keywords

references

Denniss A M,Rothery D A, et al. Lithological discrimination using landsat and JERS-1 SWIR data in the Oman Ophiolite[A]. The Tenth Thematic Conference on Geological Remote Sensing[C], Texas:1994.9-12.

王思贤,曾发龙.平滑图像噪声的插值滤波法[J].电子科学学刊,2000,22(3):411-415.

Ballerd D H. Generalizing the Hough transform of detect arbitrary shape[J]. Patt. Recog, 1981, 13(2):111-112.

张贤达.现代信号处理[M].北京:清华大学出版社,1995.

Views

309

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Generate adversarial network for super-resolution reconstruction of remote sensing images by fusing edge enhancement and non-local modules

Fast extraction of buildings from remote sensing images by fusion of CNN and Transformer

Fusion of fractal geometric features Resnet remote sensing image building segmentation

Building segmentation in remote sensing image based on multiscale-feature fusion dilated convolution resnet

Research on oil spill monitoring of multi-source remote sensing image based on deep semantic segmentation

Related Author

LIU Jie

QI Ruo

HAN Ke

ZHANG Yunzuo

GUO Wei

WU Cunyu

Shengjun XU

Ruoxuan ZHANG

Related Institution

School of Computer and Information Engineering， Harbin University of Commerce

College of Measurement and Control Technology and Communication Engineering， Harbin University of Science and Technology

School of Information Science and Technology， Shijiazhuang Tiedao University

Hebei Key Laboratory of Electromagnetic Environmental Effects and Information Processing， Shijiazhuang Tiedao University

College of Information and Control Engineering，Xi'an University of Architecture and Technology

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰