## 3 Experimental Results
### The Experiment Procedure  
The SLC quad-polarimetric SAR data was firstly multi-looked, and then the covariance matrix and coherency matrix of the data samples were generated. As mentioned before, 10 features are extracted and saved as a 10-dimension vector for each pixel.  
As shown in Figure 5, the 24,000 data samples were picked up from the image, including 12,000 verified positive (mineral oil) and 12,000 negative (clean sea surface and biogenic slick) samples. The data samples were picked by squared boxes with the size of 20 \(\times\) 20 for convenience and keeping the purity of the sample, and then their order was shuffled.  
In order to test the performance of different algorithms and avoid over-fitting, a six-fold cross-validation was applied. We first divided the training set into six subsets equally. Then the five-sixths of the data samples were used as training set and the rest were taken as testing set. Sequentially, we repeat the classification and another one-sixth data sample were used as testing set. The experiment was conducted six times until each instance of the whole training set is predicted once. Finally, the cross-validation accuracy is the overall percentage of data which are correctly classified.  
In order to test the performance of these algorithms on smaller sample sizes, the whole dataset was divided into smaller groups. All the 24,000 data samples were divided into 5 and 25 groups randomly. Then classifications were conducted on these groups, namely 4000 training, 800 testing and 800 training, and 160 testing samples respectively. In the experiment, the classification accuracy of smaller sample size was obtained by averaging the classification result on each group respectively.  
In the experiment, two previously introduced deep learning algorithms (i.e., DBN and SAE) were tested on their performance of oil spill detection and classification. In addition, two traditional supervised classifiers including neural network (NN) and SVM were compared.  
Figure 4: Pauli RGB image of RADARSAT-2 data. (RADARSAT-2 Data and Products © Macdonald, Dettwiler and Associates Ltd., Vancouver, BC, Canada, 2011—All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency).  
The key parameters of these applied classifiers are shown in Tables 2-5:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sizes of SAE layers & [8; 6] \\ Activation function SAE & Sigmoid \\ Learning rate of SAE & 1 \\ Input Zero Masked Fraction of SAE & 0.5 \\ Number of epochs when training SAE & 10 \\ Batch size when training SAE & 100 \\ Size of the whole network & [10; 8; 6; 2] \\ Activation function of the neural network & Sigmoid \\ Learning rate when making fine-tuning & 3 \\ Number of epochs when making fine-tuning & 100 \\ Batch size when making fine-tuning & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings of the stacked autoencoder (SAE).  
Figure 5: Demonstration of a selected area for analysis (taking VV\({}^{2}\) image as background); 24,000 pixels are picked as data samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sizes of layers & [10; 8; 6; 2] \\ Activation function & Sigmoid \\ Learning rate & 1 \\ Number of epochs & 100 \\ Batch size & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings of the neural network.  
LIBSVM-a library for Support Vector Machines [33] was used to implement the SVM algorithm. The parameters \(C\) and \(\gamma\) were derived by shrinking heuristics search technique. The neural network has ten input neurons, two hidden layers and two output neurons. The initialization of the former layers of deep learning algorithms SAE and DBN were carried out by unsupervised pretraining, and then the outputs were connected to a neural network with two output neurons.