Superpixel-based coastline extraction in SAR images with speckle noise removal

Coastline extraction in Synthetic aperture radar (SAR) images is a fundamental and challenging task due to the speckle noise. In this paper, we propose a new method for automatic coastline extraction in SAR images. In our method, we combine K-means and speckle noise removal methods together to increase the dissimilarity between sea and land. To enhance the robustness to speckle noise, and preserve the targets boundaries, we treat superpixels as basic regions instead of pixels in traditional pixel-based methods. Finally, an adaptive threshold is applied to classify these regions into sea or land. Based on the classifications, a canny detector is employed to detect the coastline. We evaluate our proposed method on SAR images and the improved coastline extraction method superpixel-based is verified on remote sensing images with RGB channels. The experimental results demonstrate its superior performance on coastline extraction.