Synthetic Aperture Radar (SAR) data has been used for decades to detect oil slicks and monitoring sea ice. With increased oil and gas exploration in the Arctic follows higher risk for oil spills. Knowledge of the sea ice and oil spills is important for making clever and efficient decisions in a hectic and non-hectic operational situation. The potential and limitations of the hybrid-polarity (HP) SAR ...

Utilizing several Synthetic Aperture Radar (SAR) missions will provide a data set with higher temporal resolution. It is of great importance to understand the difference between various available sensors and polarization modes and to consider how to homogenize the data sets for a following combined analysis. In this study, a uniform and consistent analysis across different SAR missions is carried ...

This paper addresses the problem of up-scaling full polarimetric (quad-pol) parameters from small quad-pol synthetic aperture radar (SAR) scenes to large dual-pol scenes, using a sophisticated Machine Learning (ML) method, namely the Gaussian Process Regression (GPR). The approach is to let the GPR model learn the relationships between the dual-pol input data and the quad-pol parameters on a quad-pol ...

The objective of this paper is to investigate the dependency of oil spill observations in polarimetric SAR data on imaging geometry, i.e., on incidence angle and look direction relative to the wind. The study is based on quad-polarization data acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar over experimental oil slicks under relatively high winds of 10–12 m/s over an 8-hour period. ...

The effects of both system additive and multiplicative noise on the X-, C-, and L-band synthetic aperture radar (SAR) data covering oil slicks are examined. Prior studies have attempted to characterize such oil slicks, primarily through analysis of polarimetric SAR data. In this article, we factor in system noise that is added to the backscattered signal, introducing artifacts that can easily be ...

We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a ...

In this paper, we investigated the capabilities of the Gaussian Process Regression (GPR) algorithm in predicting of two quad-polarimetric parameters (relevant for sea ice analysis) from 6-dimensional dual-polarimetric input vectors. The GRP is trained on few hundred samples selected randomly from an image subset, and tested on the entire image. The performance is assessed by visual comparisons, and ...

Log-cumulants have proven to be an interesting tool for evaluating the statistical properties of potential oil spills in polarimetric Synthetic Aperture Radar (SAR) data within the common horizontal (H) and vertical (V) polarization basis. The use of first, second, and third order sample log-cumulants has shown potential for evaluating the texture and the statistical distributions, as well as ...

New quantitative and semiautomated methods for analyzing oil slick evolution using a time series of L-band synthetic aperture radar (SAR) images with short repeat time are developed and explored. In this study, two methods that are complementary in terms of identifying temporal changes within an oil slick are presented. The two methods reflect two ways of evaluating the oil slicks. The first method ...