Assessment and enhancement of SAR noncoherent change detection of sea-surface oil spills
Frost, J. David
Barnes, Christopher F.
MetadataShow full item record
Oil spills are one of the most dangerous catastrophes that threaten the oceans. Therefore, detecting and monitoring oil spills by means of remote sensing techniques that provide large-scale assessments is of critical importance to predict, prevent, and clean oil contamination. In this study, the detection of an oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. These algorithms and the probabilistic selection of threshold criteria are reviewed and discussed. A recently offered change detection method that depends on generating change maps of two images in a temporal sequence is used. An initial change map is obtained by cumulatively adding sequences in such a manner that common change areas are excluded and uncommon change areas are included. A final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double-check mechanism to the conventional approach and, thus, reduce the probability of false alarm while enhancing change detection. The algorithms are tested on 2010 Gulf of Mexico oil spill imagery. It is shown that the intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that the proposed method can reduce the probability of false alarm.
Showing items related by title, author, creator and subject.
Battle Damage Assessment based on self-similarity and contextual modeling of buildings in dense urban areas Ateş, Hasan Fehmi; İmamoǧlu, Mümin; Kahraman, Fatih (IEEE, 2016)Assessment of battle damages is significant both for tactical planning and for after-war relief efforts. In this study damaged buildings are detected using self-similarity descriptor in pre- and post-war satellite images. ...
Tek, Faik Boray; İyidir, İbrahim Kamil; Kırcalı, Doğan (Springer-Verlag, 2012-10-07)This paper presents a novel vision based obstacle detection algorithm that is adapted from a powerful background subtraction algorithm: ViBe (VIsual Background Extractor). We describe an adaptive obstacle detection method ...
Kırcalı, Doğan; Tek, Faik Boray (SPRINGER-VERLAG BERLIN, 2014)Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a very simple but robust ground plane detection method based on depth information obtained using anRGB-Depth sensor. ...