Detection of Anomalous Particles from Deepwater Horizon Oil Spill Using SIPPER3 Underwater Imaging Platform

The aim of this study is to investigate a data mining approach to help assess consequences of oil spills in the maritime environment. The approach under investigation is based on the visual detection of suspected oil droplets in the water column adjacent to the Deepwater Horizon oil spill. Our method detects particles in the water, classifies them and provides an interface for the visual display and detailed examination. The particles can be plankton, marine snow, oil droplets and more. The focus of this approach is to generalize the methodology utilized for plankton classification using SIPPER (Shadow Imaging Particle Profiler and Evaluation Recorder). The SIPPER, which has been in use by marine scientists for the last decade, allows the timely extraction and identification of millions of images per deployment as scanned by its underwater sensor. It can be deployed at various depths. In this paper, we report on the application of image processing and machine learning techniques to discern suspected oil droplets from plankton and other particles present in the water. We train the classifier on the data obtained during one of the first research cruises to the site of the Deepwater Horizon oil spill. Suspected oil droplets were visually identified in SIPPER images by an expert. The classification accuracy of the suspected oil droplets is reported and analyzed. Our approach reliably finds oil when it is present. It also classifies some particles (air bubbles and some marine snow), up to 2.8%, as oil in clear water. You can reliably find oil by visually looking at the examples put in the oil class ordered by probability, in which case oil will be found in the first 10% of images examined. General Terms Algorithms, Measurement, Documentation, Performance, Design, Verification.