Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

Abstract

Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a "Wishart mixture model" (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil-free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches and that the GC algorithm provides the best optimization.