Science Service System

Summary of Proposal MTH2653

TitleOil Spill Detection and Tracking using Lipschitz Regularity and Multiscale Techniques in Synthetic Aperture Radar Imagery
Investigator Ajadi, Olaniyi - University of Alaska Fairbanks, Geology and Geophysics
Team Member
Dr Franz, Meyer - University of Alaska Fairbanks, Geophysical institute
Werner, Charles - Gamma Remote Sensing,
SummaryThis research is to presents a promising new oil spill detection and tracking method that will be based on time series of SAR images. Through the combination of a number of advanced image processing techniques, the develop approach will be able to mitigate some of the limitations of SAR-based oil-spill detection and enables fully automatic spill detection and tracking across a wide range of spatial scales. The method combines an initial automatic texture analysis with a consecutive change detection approach based on multi-scale image decomposition. The first step of the approach, a texture transformation of the original SAR images, will be performed in order to normalize the ocean background and enhance the contrast between oil-covered and oil-free ocean surfaces. The Lipschitz regularity (LR), a local texture parameter, will be used here due to its proven ability to normalize the reflectivity properties of ocean water and maximize the visibly of oil in water. To calculate LR, the images are decomposed using two-dimensional continuous wavelet transform (2D-CWT), and transformed into Holder space to measure LR. After texture transformation, the now normalized images will be inserted into our multi-temporal change detection algorithm. The multi-temporal change detection approach is a two-step procedure including (1) data enhancement and filtering and (2) multi-scale automatic change detection. The data that I will be using will be TerraSAR-X and this project is been funded by NASA.

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DLR 2004-2016