Coastline detection in SAR images suffers from the presence of speckle effect and the strong signal return from a wind-roughened and/or wave-modulated sea. It has already been recognized that ocean areas in SAR images are almost always much more homogeneous in grey levels than land areas. Therefore, features reflecting the roughness of an image can be very useful for ocean-land separation. To represent its geometric property, an SAR signal is modeled by a spatial chaotic model (SCM) and characterized by its fractal dimension. The differential box-counting (DBC) technique is adopted to estimate fractal dimension in this paper. Observations provided by SAR sensors are uncertain due to changing illumination conditions at different locations. Besides, the selection of window size  M  and grid size s in DBC provides an additional degree of uncertainty. Both the uncertainty involved in the measurements and the uncertainty involved in the selection of  M  and s motivate us of integrating type-2 fuzzy sets with the SCM to achieve an appropriate selection of the threshold for ocean-land segmentation. The proposed approach is applied to an SAR image for coastline detection as a demonstration. The final result shows that the coastline detected coincide very well with the true situation when it is overlaid on the original image. Besides, the detected coastline also agrees very well with the terrestrial measurements.
['Yu-Chang Tzeng', 'Dana Chen', 'Kun-Shan Chen']
Integration of Spatial Chaotic Model and Type-2 Fuzzy Sets to Coastline Detection in SAR Images