Segmentation for Synthetic Aperture Radar (SAR) is a very important aspect for satellite radar images. It is important to separate areas that be clustered based on characteristics or features of the image. Nowadays, there have a lot of segmentation techniques of SAR images. In this thesis, the techniques being investigated are edge adaptive smoothing, watershed transform, mean shft segmentation and region merging via boundary melting techniques which is the best among segmentation techniques. The comparison or evalution among the techniques is in term of number of edges retained in the segmented images and also in visual inspection. In this research, we use two different type of images, which is real SAR image and non-real SAR image (house). Results generated from this research has shown that edge adaptive smoothing is the best one compared to the other segmentation techniques
