%0 Journal Article
%4 sid.inpe.br/mtc-m21b/2015/11.25.16.08
%2 sid.inpe.br/mtc-m21b/2015/11.25.16.08.58
%@doi 10.1016/j.knosys.2015.07.030
%@issn 0950-7051
%T Optical images-based edge detection in Synthetic Aperture Radar images
%D 2015
%8 Oct.
%9 journal article
%A Silva Júnior, Gilberto Pedro da,
%A Frery, Alejandro C.,
%A Sandri, Sandra Aparecida,
%A Bustince, Humberto,
%A Barrenechea, Edume,
%A Marco-Detchart, Cedric,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress gp7junior@gmail.com
%@electronicmailaddress
%@electronicmailaddress sandra.sandri@inpe.br
%B Knowledge Based Systems
%V 87
%P 38-46
%K Edge detection, SAR images, Computational Intelligence, Gravitational method.
%X We address the issue of adapting optical images-based edge detection techniques for use in Polarimetric Synthetic Aperture Radar (PoISAR) imagery. We modify the gravitational edge detection technique (inspired by the Law of Universal Gravity) proposed by Lopez-Molina et al., using the non-standard neighbourhood configuration proposed by Fu et al., to reduce the speckle noise in polarimetric SAR imagery. We compare the modified and unmodified versions of the gravitational edge detection technique with the well-established one proposed by Canny, as well as with a recent multiscale fuzzy-based technique proposed by Lopez-Molina et al. We also address the issues of aggregation of gray level images before and after edge detection and of filtering. All techniques addressed here are applied to a mosaic built using class distributions obtained from a real scene, as well as to the true PoISAR image; the mosaic results are assessed using Baddeley's Delta Metric. Our experiments show that modifying the gravitational edge detection technique with a non-standard neighbourhood configuration produces better results than the original technique, as well as the other techniques used for comparison. The experiments show that adapting edge detection methods from Computational Intelligence for use in PoISAR imagery is a new field worthy of exploration.
%@language en
%3 2015-silva junior.pdf