Classification of Oil Slicks and Look-Alike Slicks: A Linear Discriminant Analysis of Microwave, Infrared, and Optical Satellite Measurements

Abstract

We classify low-backscatter regions observed in Synthetic Aperture Radar (SAR) measurements of the surface of the ocean as either oil slicks or look-alike slicks (radar false targets). Our proposed classification algorithm is based on Linear Discriminant Analyses (LDAs) of RADARSAT-1 measurements (402 scenes off the southeast coast of Brazil from July 2001 to June 2003) and Meteorological-Oceanographic (MetOc) data from other earth observation sensors: Advanced Very High Resolution Radiometer (AVHRR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Quick Scatterometer (QuikSCAT). Oil slicks are sea-surface expressions of exploration and production oil, ship- and orphan-spills. False targets are associated with environmental phenomena, such as biogenic films, algal blooms, upwelling, low wind, or rain cells. Both categories have been interpreted by domain-experts: mineral oil (n = 350; 45.5%) and petroleum free (n = 419; 54.5%). We explore nine size variables (area, perimeter, etc.) and three types of MetOc information (sea surface temperature, chlorophyll-a, and wind speed) that describe the 769 samples analyzed. Seven attribute–domain combinations are tested with three non-linear transformations (none, cube root, log10), with and without MetOc, adding to 39 attribute subdivisions. Classification accuracies are independent of data transformation and improve when selected size attributes are combined with MetOc, leading to overall accuracies of ~80% and sound levels of sensitivity (~90%), specificity (~80%), positive (~80%) and negative (~90%) predictive values. The effectiveness of this data-driven attempt supports further commercial or academic implementation of our LDA algorithm.

Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

“…In practice, each combination was considered as an individual "LDA algorithm". The data combinations in our algorithms are different to those explored in earlier studies, but are similar in number of combinations in three other papers, i.e., 32 [39] + 61 [40] + 39 [42] = 132. Of the combinations analyzed here (60 + 54 = 114), only nine have been previously investigated, but were modified as discussed below.…”

“…(ii) distinguish oil spills from look-alike slicks observed in RADARSAT-1 scenes off the coast of Brazil (Campos Basin) [42]. We explored many data combinations: 60 combinations of variables (Experiment 1) and 54 combinations of data transformations (Experiment 2).…”

“…A tabular remote sensing dataset, including microwave, infrared, and visible satellite measurements, was exploited here. This dataset was first utilized by Bentz [46], and later explored by Moutinho [47] and Carvalho et al [42]. An important characteristic of this dataset for our study is the classification of oil spills vs. look-alikes based on expert interpretation.…”

“…Each of these 20 combinations had all variables subjected to the same data transformation (i.e., non-transformed, cube root, or log 10 ), thus forming 60 combinations. Combinations previously explored in Carvalho et al [42] are indicated (#). See also Section 3.1.2.…”

“…The positive results of the seep-spill LDA studies, combined with the simplicity and power of the linear analyses to classify oil slicks identified in satellite imagery, form the justifications to retain this linear classification technique in the research reported here, where we study the classification between oil spills and look-alike slicks. While LDAs were applied to remotely sensed features obtained with the Canadian RADARSAT-2 to classify seeps and spills in Gulf of Mexico waters [41], here LDAs are applied to features retrieved in images of the Canadian RADARSAT-1 to distinguish the presence of mineral oil on the sea-surface from other petroleum-free features off the Brazilian coast [42].…”