Edge detection and localization in SAR images: a comparative study of global filtering and active contour approaches

1998 ◽  
Author(s):  
Olivier Germain ◽  
Philippe Refregier
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Active Contour ◽  
Sar Images ◽  
Detection And Localization

Edge detection in SAR images of transform domain

2009 ◽  
Vol 29 (9) ◽  
pp. 2402-2405 ◽  
Author(s):  
Zhen MEI ◽  
Wei LIN ◽  
Rui-xia WANG
Keyword(s):  
Edge Detection ◽  
Transform Domain ◽  
Sar Images

scholarly journals A Proposed Approach to Determine the Edges in SAR images

2020 ◽  
pp. 185-192
Author(s):  
Rabab Farhan Abbas
Keyword(s):  
Edge Detection ◽  
Electromagnetic Waves ◽  
Operator Method ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Edge Image ◽  
Sar Images ◽  
Wavelet Method ◽  
Radar Images ◽  
Sobel Operator

Radar is the most eminent device in the prolonged scattering era The mechanisms involve using electromagnetic waves to take Synthetic Aperture Radar (SAR) images for long reaching. The process of setting edges is one of the important processes used in many fields, including radar images, which assists in showing objects such as mobile vehicles, ships, aircraft, and meteorological and terrain forms. In order to accurately identify these objects, their edges must be detected. Many old-style methods are used to isolate the edges but they do not give good results in the  determination process. Conservative methods use an operator to detect the edges, such as the Sobel operator which is used to perform edge detection where the edge does not appear well.      The proposed method which combines Ridgelet transform, Bezier curve and Sobel operator is used to detect edges very efficiently. Ridghelet transform resolves the harms in the wavelet transform and it can well detect the edges in images. Bezier curve can profit gradual variation of the data and their mutability. Hence, the efficiency of the edged image is improved and, when used with Sobel operator, the quality of the edge image become very good. The data show that the advocated method has superior fallouts over the Sobel edge detection and the wavelet method in both subjective and impartial experiments. While the Peak Signal to Noise Ratio(PSNR) values were equal to 9.3812, 9.8918, 9.6521 and 9.0743using the Sobel operator method and to10.2564, 10.7927, 10.5612and 10.8633 using the wavelet method, they were increased in the proposed method to 12.6542, 12.9514, 12.8574 and 12.3013 respectively.

Automatic Object Classification and Image Retrieval by Sobel Edge Detection

2014 ◽  
pp. 196-201
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Object Classification ◽  
Similarity Coefficient ◽  
Cosine Similarity ◽  
Retrieval Algorithms ◽  
Sobel Edge Detection ◽  
Classification Tool

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

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