International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Kim et al., 2010), (3) segmentation techniques (Barni et al., 1995, 
Solberg et al., 2007), (4) slick feature extraction (Fiscella et al., 
2000, Del Frate et al., 2000). Some algorithms also combine 
ocean drift models to assist data analysis (Espedal, 1999, Cheng 
et al., 2011). In addition to SAR intensity, co-polarization differ- 
ences of multiple polarization SAR data is also suggested for oil 
spill detection (Migliaccio et al., 2009). 
In this paper, our focus is not on developing a method that per- 
forms better than any one of the methods mentioned earlier, but 
instead to develop a practical framework where results from dif- 
ferent methods and sources can be combined to provide a joint 
solution, which is likely to have less uncertainty. The method 
can be easily extended to include any geospatial observation (e.g. 
optical remote sensing, ground measurements), however in this 
paper our focus is on SAR imagery. The algorithm is composed 
of three calculation steps: (1) Pixel (point) probability, (2) spatial 
probability, and (3) spatio-temporal probability. Point probabil- 
ity is calculated based on normalized radar cross section, where 
darker pixels get higher probabilities for oil contamination (Barni 
et al., 1995). Spatial probability is based on the damping ratio 
given the current wind conditions and imaging parameters (Gade 
et al., 1998, Kim et al., 2010). Point and spatial analysis results 
are then combined to provide a joint probability for oil slick at 
each acquisition. The spatio-temporal probability is estimated 
from multiple SAR acquisitions separated shortly in time, result- 
ing in a time varying probability of oil slick over target area. All 
analysis in this paper is done over intensity calibrated, geocoded 
SAR imagery. Images are calibrated to normalized radar cross 
section (NRCS). The imagery is resampled to a common geome- 
try using a sinc interpolator, and land-masked using Global, Self- 
consistent, Hierarchical, High-Resolution Shoreline data (GSHHS) 
(Wessel and Smith, 1996). 
First and second steps of the algorithm are iterative, and are per- 
formed at the same time. The first step of the algorithm is a sim- 
ple dark-object selection routine based on intensity thresholding. 
At this step, dark areas of the image are assigned a higher proba- 
bility for an oil spill. The initial threshold for the first step is the 
noise equivalent sigma zero (NESZ), which is the sensors noise 
floor. A probability value for each pixel is assigned based on it’s 
intensity such that: 
P(Wi|es) = 
P(Oloo) 
(co — min(co))/(T — min(co)) (1) 
1 — P(W|oo) (2) 
| 
where P(W |oo) is probability of oil-free water given the NRCS, 
T 'is the threshold, and P(Oloo) is the probability of oil given the 
NRCS is the complement of P(W oo). The P(W |oo) is modi- 
fied by bringing all larger values to 1, constraining the probability 
values between 0 and 1. Furthermore, iterations start using a mul- 
tilooked imagery, to reduce the effect of speckle noise. It is worth 
noting that the multilooking operation changes the dynamic range 
of the radar imagery. In order to keep the thresholds equivalent at 
each iteration, multilooked images are further calibrated to have 
the same dynamic range as the full resolution image. 
The second step of the algorithm starts with calculation of damp- 
ing factor after Gade et al. (Gade et al., 1998, Kim et al., 2010). 
The estimated damping factors at different wind speeds as a func- 
tion of Bragg wavenumbers are shown in Figure 1. Figure 1 also 
shows the maximum theoretical observation range for Radarsat- 
2, Envisat-ASAR, and Alos-PALSAR SAR systems. The Bragg 
wavenumber is defined as (Gade et al., 1998): 
ks = 2ko sin(¢) (3) 
where kg is the Bragg wavenumber, ko is the radar wavenum- 
64 
ber, and ¢ is the incidence angle. Figure 1 shows the maximum 
expected damping amount which would be observed when the an- 
gle between the wind, and radar wave is zero. ALOS-PALSAR is 
not expected to be effective in mild wind conditions, as shown 
in the figure. The SAR sensors on-board Radarsat-2 and En- 
visat are both C-Band, and therefore cover similar regions in the 
wavenumber domain. The slightly larger coverage of Radarsat-2 
is due to it’s larger range of incidence angles. 
=> 
N 
  
oo 
BA 
Damping Factor [dB] 
  
  
  
  
0 200 300 
Bragg Wavenumber 
Figure 1: Estimated damping factor as a function of Bragg 
wavenumber. The theoretical range of SAR systems used in the 
system are marked with horizontal lines. Each curve represents a 
different wind velocity. 
Wind is an important factor to estimate the damping factor, which 
is also dependent on the relative angle between radar wave and 
wind direction. Figure 2 shows a plot of expected damping fac- 
tors at speeds between 2.5 m/s and 12.5 m/s. The relative 
wind direction is plotted at counter-clockwise increasing angles, 
starting from zero at the horizontal axis. In this study we use 
wind speed, direction and ocean wave group velocity data from 
National Data Buoy Center station 42040, located at 29.122N, 
88.207W, about 40km NNW of the Deepwater Horizon oil rig. 
180 
  
Figure 2: Estimated damping factor plotted against the relative 
angle between radar and wind direction. Radial distance from the 
center indicates the wind speed. 
The first and second steps of the algorithm are run iteratively at 
five different spatial resolutions. The analysis is performed in a 
pyramid structure with five different stages. At each stage the im- 
age is multilooked to the power of two, such that at the first stage