Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/487012
Title: SAR image despeckling using edge detection and feature clustering in bandelet domain
Authors: Zhang, Wenge;Liu, Fang;Jiao, Licheng;Hou, Biao;Wang, Shuang;Shang, Ronghua
subject: Canny operator;Edge detection;Fuzzy clustering;Image processing;Speckle;Synthetic aperture radar (SAR);Translation-invariant bandelet transform;10.1109/LGRS.2009.2028588
Year: 2010
Publisher: Ieee
Abstract: To effectively preserve the edges of a synthetic aperture radar (SAR) image when despeckling, an algorithm with edge detection and fuzzy clustering in the translation-invariant second-generation bandelet transform (TIBT) domain is proposed in this letter. A Canny operator is first utilized to detect and remove edges from the SAR image. Then, TIBT and fuzzy C-mean clustering are employed to decompose and despeckle the edge-removed image, respectively. Finally, the removed edges are added to the reconstructed image. The algorithm suggests each coefficient in high-frequency subbands as the clustering feature, proposes a calculation method of the best clustering number, and defines the signal and noise in the clustering results. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
Description: 
URI: http://localhost/handle/Hannan/307534
http://localhost/handle/Hannan/487012
ISSN: 1545-598X VO - 7
Appears in Collections:2010

Files in This Item:
File SizeFormat 
AL1725729.pdf436.71 kBAdobe PDF
Title: SAR image despeckling using edge detection and feature clustering in bandelet domain
Authors: Zhang, Wenge;Liu, Fang;Jiao, Licheng;Hou, Biao;Wang, Shuang;Shang, Ronghua
subject: Canny operator;Edge detection;Fuzzy clustering;Image processing;Speckle;Synthetic aperture radar (SAR);Translation-invariant bandelet transform;10.1109/LGRS.2009.2028588
Year: 2010
Publisher: Ieee
Abstract: To effectively preserve the edges of a synthetic aperture radar (SAR) image when despeckling, an algorithm with edge detection and fuzzy clustering in the translation-invariant second-generation bandelet transform (TIBT) domain is proposed in this letter. A Canny operator is first utilized to detect and remove edges from the SAR image. Then, TIBT and fuzzy C-mean clustering are employed to decompose and despeckle the edge-removed image, respectively. Finally, the removed edges are added to the reconstructed image. The algorithm suggests each coefficient in high-frequency subbands as the clustering feature, proposes a calculation method of the best clustering number, and defines the signal and noise in the clustering results. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
Description: 
URI: http://localhost/handle/Hannan/307534
http://localhost/handle/Hannan/487012
ISSN: 1545-598X VO - 7
Appears in Collections:2010

Files in This Item:
File SizeFormat 
AL1725729.pdf436.71 kBAdobe PDF
Title: SAR image despeckling using edge detection and feature clustering in bandelet domain
Authors: Zhang, Wenge;Liu, Fang;Jiao, Licheng;Hou, Biao;Wang, Shuang;Shang, Ronghua
subject: Canny operator;Edge detection;Fuzzy clustering;Image processing;Speckle;Synthetic aperture radar (SAR);Translation-invariant bandelet transform;10.1109/LGRS.2009.2028588
Year: 2010
Publisher: Ieee
Abstract: To effectively preserve the edges of a synthetic aperture radar (SAR) image when despeckling, an algorithm with edge detection and fuzzy clustering in the translation-invariant second-generation bandelet transform (TIBT) domain is proposed in this letter. A Canny operator is first utilized to detect and remove edges from the SAR image. Then, TIBT and fuzzy C-mean clustering are employed to decompose and despeckle the edge-removed image, respectively. Finally, the removed edges are added to the reconstructed image. The algorithm suggests each coefficient in high-frequency subbands as the clustering feature, proposes a calculation method of the best clustering number, and defines the signal and noise in the clustering results. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
Description: 
URI: http://localhost/handle/Hannan/307534
http://localhost/handle/Hannan/487012
ISSN: 1545-598X VO - 7
Appears in Collections:2010

Files in This Item:
File SizeFormat 
AL1725729.pdf436.71 kBAdobe PDF