Polarimetric SAR oil spill detection based on deep networks

Abstract

Polarimetric SAR remote sensing provides an outstanding capability of oil spill detection and classification for its advantages in distinguishing mineral oil and biogenic look-alikes. In this paper, deep learning algorithms including Stacked Auto-Encoder (SAE) and Deep Believe Network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through the processes of layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during Norwegian oil-on-water exercise, in which verified mineral, emulsions, and biogenic slicks were provided. The results show that oil spill classification achieved by deep networks outperformed support vector machine (SVM) and traditional artificial neural network (ANN) with similar parameter settings, especially when the number of training data samples is limited.