Ramona Pelich

TL;DR: Compared with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas.

TL;DR: In this paper, a case study based on four flood events of the River Severn (United Kingdom) is presented, where the authors use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values.

TL;DR: This paper studies the performances of different ship detectors based on adaptive threshold algorithms based on various clutter distributions and assessed automatically with a systematic methodology using large datasets of medium resolution SAR images and AIS data as ground truths.

TL;DR: The fractional Fourier transform makes it possible to represent the SAR signal in a rotated joint time-frequency plane and performs optimal processing and analysis of these residual chirp signals, and the along-track defocus can be compensated for and the target's azimuthal speed estimated.

TL;DR: An automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed and indicates a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40% for smaller size vessels.