Star Control Points Optimization on Remote Sensing Image Geometric Rectification

2017 ◽  
Author(s):  
Xiangli Tan ◽  
Jungang Yang ◽  
Xinpu Deng
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Control Points ◽  
Geometric Rectification

Automatic Matching Method of Control Points for AVHRR Remote Sensing Image

2014 ◽  
Author(s):  
Ying Xia ◽  
◽  
Linjun Zhu ◽  
Xiaobo Luo ◽  
Hae Young Bae ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Control Points ◽  
Matching Method ◽  
Automatic Matching

scholarly journals STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE

2016 ◽  
Vol XLI-B2 ◽  
pp. 305-312
Author(s):  
Weili Jiao ◽  
Tengfei Long ◽  
Saiguang Ling ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Traditional Method ◽  
Evaluation Method ◽  
Control Point ◽  
Remote Sensing Image ◽  
Error Distribution ◽  
Distribution Model ◽  
Control Points ◽  
Mean Square Errors

It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.

scholarly journals STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE

2016 ◽  
Vol XLI-B2 ◽  
pp. 305-312
Author(s):  
Weili Jiao ◽  
Tengfei Long ◽  
Saiguang Ling ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Traditional Method ◽  
Evaluation Method ◽  
Control Point ◽  
Remote Sensing Image ◽  
Error Distribution ◽  
Distribution Model ◽  
Control Points ◽  
Mean Square Errors

It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.

Control Point Extraction in the Remote Sensing Image via Adaptive Filter

2013 ◽  
Vol 411-414 ◽  
pp. 1267-1276
Author(s):  
Lei Lei Geng ◽  
De Shen Xia ◽  
Quan Sen Sun ◽  
Kai Yuan
Keyword(s):  
Remote Sensing ◽  
Nearest Neighbor ◽  
Rapid Development ◽  
Control Point ◽  
Remote Sensing Image ◽  
Ground Control ◽  
Reference Image ◽  
Control Points ◽  
Ground Control Points ◽  
Geometric Precision

With the rapid development of the remote sensing satellite, the size and resolution of remote sensing image grow increasingly. The evaluation of image quality requires precise information of ground control points extracted from remote sensing image and reference image. Therefore, we propose an adaptive Wallis enhancement based on radiation-parameters to increase the number of ground control points and to improve the matching precision. First, feature vectors of sub-region are constructed by computing image radiation-parameters, and then the sub-region terrain in the remote sensing image can be recognized using nearest neighbor classifier. Second, according to specific type of sub-region terrain, we enhance images using adaptive Wallis filter with local parameters. Finally, two-level matching method is used to extract and match the control points. The experiments show that compared with existing Wallis filter which are based on global parameters, our method gets better results in the detail enhancement on ZY-3 image so that more and higher accurate ground control points can be effectively extracted to achieve the evaluation of geometric precision automatically and accurately.

Sign in / Sign up

Export Citation Format

Share Document