Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills

Abstract

Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

“…This avoids the additional cross-comparison effects concerning technical differences of two the satellites. This dataset is the same one exploited earlier [31,32], and includes images from 2008 to 2012 that contain 4562 oil slicks; coincidently the same unbalanced proportion of the entire database: 1994 seeps (44%) and 2568 spills (56%). The experimental methodology applied to evaluate the outcomes of our LDAs uses all 4562 oil slicks for training the algorithms.…”

“…• [29,33,34] describe the dataset; • [29,30] discuss the original exploratory multivariate data analysis-referred to as the initial exploratory analysis; and • [31,32] present further developments of the original analysis in a more controlled fashion-referred to as the first refined study.…”

“…At the outset of the initial analysis [29,30], the full SAR signature set was investigated together as one entity, then, all calibrated products (four) were analyzed together for each backscatter coefficient (three). On the other hand, only σ • amp with no despeckle filter (SIG.amp) was explored in the refined study [31,32], thus providing a firmer basis and more control in the understanding of the seep-spill categorization process.…”

“…[27,28]. A third challenge has emerged as a recent subject matter: the separation of oil (seeps) from oil (spills)-i.e., the discrimination of the slick category: seeps vs. spills [29][30][31][32][33][34].…”

“…Our objective in this paper is to use a simple, but mathematically robust multivariate data analysis technique (i.e., build an algorithm based on linear discriminant analysis (LDA) applied to SAR-derived measurements) to seek improvements in slick category differentiation. Evolving from our earlier investigations, which empirically reached almost 70% of overall accuracy to separate seeps from spills in Campeche Bay [29][30][31][32][33][34], we set out an exploratory data analysis to try, with more rigor, accuracy improvements in relation to our previous approach. The investigative nature of the research reported here aims to answer four scientific questions:…”