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 discriminating oil from look-alikes. Log-cumulants are cumulants derived in the log-domain and can be applied to both single-polarization and multipolarization SAR data. This study is the first to investigate the differences between hybrid-polarity (HP) and full-polarimetric (FP) modes based on the sample log-cumulants of various oil slicks and open water from nine Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) scenes acquired off the coast of Norway in 2015.
The sample log-cumulants calculated from the HP intensities show similar statistical behavior to the FP ones, resulting in a similar interpretation of the sample log-cumulants from HP and FP. Approximately eight hours after release the sample log-cumulants representing emulsion slicks have become more similar to the open water compared to plant oil. We find that the sample log-cumulants of the various oil slicks and open water varies between the scenes and also between the slicks and open water. This might be due to changes in ocean and wind condition, the initial slick properties, and/or the difference in the weathering process of the oil slicks.
In this paper, we propose a strategy for ocean slick classification in SAR images operating in a hybrid-polarimetric mode. The proposed scheme is successfully applied to classify mineral and plant oil slicks in SAR data covering oil spill experiments outside Norway and the Deepwater Horizon incident in the Gulf of Mexico. Using the elements of a hybrid-polarimetric coherency matrix as features, we construct a random forest classifier from training data obtained from an SAR image covering an oil-on-water exercise in the North Sea. The results show that we area able to distinguish mineral oil from plant oil and low wind slicks, however, it is challenging to distinguish the mineral oil types emulsion and crude oil. Due to the potential of wide swath widths, we conclude that hybrid-polarity is an attractive mode for future enhanced SAR-based oil spill monitoring.
In this paper we propose a framework for fusion of very high resolution (VHR) optical aerial images, satellite images (optical or SAR) and other ancillary data (e.g. a digital elevation model) for identification and modeling of nature types typically present in mountain vegetation in Arctic alpine areas. The data fusion methodology consists of three steps. (i) Segmentation of VHR aerial photo into spectrally homogeneous regions (polygons). (ii) Estimation of complementary information for each polygon using geo-referenced data from other sources. (iii) Analysis of the constructed feature vectors. We also demonstrated the strength of satellite data by qualitatively evaluating the potential for creating high resolution snow cover maps. These maps may be used to describe important environmental variables. Using a set of data consisting of an aerial photo, two SPOT 5 images and a Radarsat-2 quad-pol image, we demonstrated the potential of the data fusion methodology by an example where the polygon-derived features were analysed using PCA.
In this paper we discuss how multisource data (wind, ocean-current, optical, bathymetric, automatic identification systems (AIS)) may be used to improve oil spill detection in SAR images, with emphasis on the use of automatic oil spill detection algorithms. We focus particularly on AIS, optical, and bathymetric data. For the AIS data we propose an algorithm for integrating AIS ship tracks into automatic oil spill detection in order to improve the confidence estimate of a potential oil spill. We demonstrate the use of ancillary data on a set of SAR images. Regarding the use of optical data, we did not observe a clear correspondence between high chlorophyll values (estimated from products derived from optical data) and observed slicks in the SAR image. Bathymetric data was shown to be a good data source for removing false detections caused by e.g. sand banks on low tide. For the AIS data we observed that a polluter could be identified for some dark slicks, however, a precise oil drift model is needed in order to identify the polluter with high certainty.