Coastline detection in synthetic aperture radar (SAR) images is crucial in many application fields, from coastal erosion monitoring to navigation, from damage assessment to security planning for port facilities. The backscattering difference between land and sea is not always documented in SAR imagery, due to the severe speckle noise, especially in 1-look data with high spatial resolution, high sea state, or complex coastal environments. This paper presents an unsupervised, computationally efficient solution to extract the coastline acquired by only one single-polarization 1-look SAR image. Extensive tests on Spotlight COSMO-SkyMed images of complex coastal environments and objective assessment demonstrate the validity of the proposed procedure which is compared to state-of-the-art methods through visual results and with an objective evaluation of the distance between the detected and the true coastline provided by regional authorities.
The potentials of SAR sensors in change detection applications have been recently strengthened by the high spatial resolution and the short revisit time provided by the new generation SAR-based missions, such as COSMO- SkyMed, TerraSAR-X, and RadarSat 3. Classical pixel-based change detection methods exploit first-order statistics variations in multitemporal acquisitions. Higher-order statistics may improve the reliability of the results, while plain object-based change detection are rarely applied to SAR images due to the low signal-to-noise ratio which characterizes 1-look VHR SAR image products. The paper presents a hybrid approach considering both a pixel-based selection of likely-changed pixels and a segmentation-driven step based on the assumption that structural changes correspond to some clusters in a multiscale amplitude/texture representation. Experiments on simulated and true SAR image pairs demonstrate the advantages of the proposed approach.
In this paper, we propose a change detection feature for an amplitude SAR image pair, based on both information theoretic (IT) assumptions and a CFAR criterion derived from the probabilistic model of the ratio image. In particular, the proposed method aims to introduce two main improvements with respect to the previous IT-based approaches. The first goal is to find a strategy to adaptively quantize the 2-D scatterplot instead of applying clustering. This is carried out by performing a preliminary partition of the image pixels according to a constant false alarm rate criterion that is based on the probabilistic model of the ratio image. The second goal is to test the proposed method in order to assess reliable performances in case of severe speckle noise and in case of small percentage of change within the scene. Therefore, experimental results have been carried out with simulated changes applied to synthetically-generated 1-look SAR images produced from an optical remote sensing image. True Cosmo-SkyMed SAR images have been also considered on a damage assessment scenario.