Optimized fuzzy cellular automata for synthetic aperture radar image edge detection

• Published in: Journal of electronic imaging Vol. 27; no. 1; p. 013030
• Main Authors: Farbod, Mohammad, Akbarizadeh, Gholamreza, Kosarian, Abdolnabi, Rangzan, Kazem
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.01.2018
• Subjects: synthetic aperture radar; edge detection; fuzzy cellular automata; machine learning
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.27.1.013030

The development of coastline detection has been the subject of several reports. An optimized fuzzy cellular automata (FCA) algorithm for SAR image edge detection, which combines newly defined cellular automata (CAs) and fuzzy rules, is proposed. An extended Moore neighborhood is used for cellular automaton. Twelve custom masks were defined to study the edge angles of the radius two neighborhood of the main pixel. Comparing these edge angles with the radius, one neighborhood of the main pixel is useful when deciding whether or not a pixel is an edge pixel. The model was tested on two sets of images. The first dataset contained optical and simulated SAR images and the second contained an Envisat ASAR image and a ScanSAR image. A 3  ×  3 Lee filter (as a preprocessing phase) was applied to each subimage containing coastlines, and the subimages were then processed using an FCA edge detector. The results were compared with those from a Sobel edge detector, Roberts edge detector, wavelet transform edge detector, and classic CA model. The results showed that the proposed method is more appropriate for edge detection of SAR images when compared with classic methods. The proposed method and wavelet transform edge detector showed good continuity, but the proposed method dealt better with speckle noise effects.