DocumentCode
526605
Title
Notice of Retraction
Feature extraction and selection for landmine detection using textures of Time-Frequency Representation
Author
Xiang Gao ; Guangrong Ji ; Chunhe Wang ; Guangyu Ji
Author_Institution
Coll. of Inf. Sci. & Eng., Ocean Univ. of China, Qingdao, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
516
Lastpage
520
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Ground Penetrating Radar (GPR) is widely used in the probe of subsurface targets, in which the Time-Frequency Representation (TFR) approaches have been proved effective for GPR signatures. Since we can treat TFR results as 2-D images, texture analysis is an applicable way for image discrimination. In this study, we propose a target detection method based on TFR textures of GPR A-scans and then select 20 descriptors to interpret TFR image into texture features for decision. The mutual correlation and discrimination ability of the descriptors are studied based on the comparative experiment. According to the results, it is clear that the twenty descriptors are redundant and can be grouped into three weak-correlated classes: 11 descriptors of the first class and 2 of the second one are helpful and recommended to use in texture discrimination while the rest can be discarded.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Ground Penetrating Radar (GPR) is widely used in the probe of subsurface targets, in which the Time-Frequency Representation (TFR) approaches have been proved effective for GPR signatures. Since we can treat TFR results as 2-D images, texture analysis is an applicable way for image discrimination. In this study, we propose a target detection method based on TFR textures of GPR A-scans and then select 20 descriptors to interpret TFR image into texture features for decision. The mutual correlation and discrimination ability of the descriptors are studied based on the comparative experiment. According to the results, it is clear that the twenty descriptors are redundant and can be grouped into three weak-correlated classes: 11 descriptors of the first class and 2 of the second one are helpful and recommended to use in texture discrimination while the rest can be discarded.
Keywords
feature extraction; ground penetrating radar; image representation; image texture; landmine detection; time-frequency analysis; 2D images; GPR A-scans; GPR signatures; TFR image; TFR textures; discrimination ability; feature extraction; feature selection; ground penetrating radar; image discrimination; landmine detection; mutual correlation; subsurface targets; target detection method; texture analysis; texture discrimination; texture features; time-frequency representation; weak-correlated classes; Time frequency analysis; Feature Extraction; Ground Penetrating Radar; Texture Analysis; Time-Frequency Representation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564690
Filename
5564690
Link To Document