## 1 Introduction
(2011) used the conformity coefficient as a binary classifier [18]. Other polarimetric features such as degree of polarization, entropy, alpha angle, and Bragg likelihood angle were also used to classify oil spills [19; 20; 21]. Some previous studies conducted automatic oil-spill classification algorithms. [PERSON] (2001) developed models to discriminate textures between oil and water by using co-occurrence textures [22]. [PERSON] et al. (2008) proposed one-class classification with an optimized feature selection algorithm and obtained a promising oil spill classification [23]. [PERSON] et al. (2000) proposed a semiautomatic detection of oil spills by neural network [24]. [PERSON] et al. (2008) developed the Textural Classifier Neural Network Algorithm (TCNNA) to map an oil spill in the Gulf of Mexico Deepwater Horizon accident [11]. [PERSON] (2013) used a genetic algorithm (GA) for automatic detection of an oil spill from ENVISAT ASAR (Advanced Synthetic Aperture Radar) data [25]. [PERSON] et al. (2013) used a Support Vector Machine (SVM) to detect oil spills based on morphological features on very limited data samples [26]. Polarimetric SAR features contain massive complementary and redundancy information. The extraction and optimization of them are closely related to the performance of oil spill classification [27]. Deep learning algorithms have very strong capabilities of exploring complex correlation between features and achieve very promising fitting result on complicated problems. It has been a very popular technique for image processing, computer vision, and natural language processing. According to the authors, deep learning has not been used in features optimization for oil spills detection based on polarimetric SAR data, and it should be a very promising research topic. Deep neural network with multilayer neuron has powerful capabilities in describing complex functions compared with shallow networks [28]. However, the traditional gradient descent technique works poorly on a deep neural network when the weights are initialized randomly. The reason is that when the derivative is calculated using the back propagation method, the magnitude of the gradient (from the output layer to the initial layer of the network) decreases dramatically as the network depth increases. As the result, the gradient of the overall loss function, with respect to the weights of the first few layers, is very small. Thus, when the gradient descent method is used, the weights of the first layers change very slowly, so that they cannot learn effectively from the samples. This problem is often referred to as \"gradient dispersion\". In 2006, [PERSON] et al. proposed the deep belief network (DBN), which is a belief network composed of Restricted Boltzmann Machine (RBM) one layer at a time, to take the advantage of complementary priors of the data. Inspired by DBN, [PERSON] et al. (2006) used a stacked autoencoder, which is a deep multilayer neural network that initialized its weights by a greedy layer-wise unsupervised training strategy [29]. Moreover, feature dimension reduction can be seen as an early fusion step. Fusion at different stages of classification procedures is a booming research field that has shown capabilities for improvement of classification results. For instance, [PERSON] et al. fused the output of nonindependent detectors to derive the optimum classification result [30]. Late fusion of scores of several classifiers could be adapted to the proposed problem as a future research work. The aims of this paper are exploring the capabilities of deep learning algorithms on polarimetric SAR-based marine oil spill detection. In Section 2, research methods including the representation of polarimetric SAR data, feature extraction methods and deep learning algorithms including DBN and SAE will be introduced. In Section 3, experiments were conducted on RADARSAT-2 data containing verified oil spills and biogenic lookalikes. The performance of different algorithms on various sample sizes for oil spill classification will be compared.