## 1 Introduction  
Polarimetric synthetic aperture radar (PolSAR) is a kind of high resolution imaging system, which can work under all weather, day-and-night conditions. The PolSAR data can be used to describe the scattering mechanism of the earth surface and provide rich information for terrain surface classification with the complex coherency matrix [1], scattering matrix [2], etc. With the development of the PolSAR system, PolSAR data such as Advanced Synthetic Aperture Radar (ASAR)/Environmental Satellite (ENVI-SAT), Phased Array L-band Synthetic Aperture Radar (PASAR)/Advanced Land Observing Satellite (ALOS) and Radar Satellite-2 are becoming more and more available, thus PolSAR image classification has become an important research topic [3]. It is a challenge to automatically extract and select features in PolSAR image classification. Traditional methods generally extract features manually per the scattering characteristics of the terrain surface. The features include radiation information [4], polarization information [5], sub-aperture decomposition [6], decomposition information [7], etc. A single feature or combined features are then fed into an appropriate classifier for classification. Using features individually cannot achieve satisfactory performance. Even if these features are combined then, the classification accuracy will not be improved due to subjectiveness. In addition, the features that can be combined are limited, and the computational complexity will grow with the increase of the amount of combined features. Therefore, traditional methods cannot make full use of the rich features of the PolSAR data to improve the classification accuracy. In recent years, the theory of deep learning has set off a wave in the field of pattern recognition. In 2006, [PERSON] et al. proposed an unsupervised greedy method based on Deep Belief Network (DBN), which trains layer by layer, solving the vanishing gradient problem caused by deep training [PERSON] et al. (2011). In 2011, [PERSON] et al. proposed the regularized deep Fisher mapping (RDFM) method per Fisher criterion to enhance the feature separability by using the neural network algorithm, which can eliminate the overfitting problem [PERSON] et al. (2011). Then, many scholars put forward a variety of deep learning models based on different application backgrounds, such as Deep Restricted Boltzmann (DRB) [PERSON] et al. (2012), Stacked Denoising Autoencoders (SDA) [PERSON] et al. (2013), and the Deep Convolutional Neural Network (CNN) [PERSON] et al. (2014). As a pillar of deep learning, the CNN is one of the best models for solving the \"perception\" issues. For example, the AlexNet model won first prize in the ImageNet ILSVRC image classification contest in 2012, which has caused widespread concern in related fields [PERSON] et al. (2012). To solve the problem of inefficient utilization of features in PolSAR image processing, some scholars introduce the deep learning framework to extract features of PolSAR images. [PERSON] et al. converted the PolSAR image into the scattering matrix, and then established multichannels for the CNN model [PERSON] et al. (2013). Afterwards, the features were extracted automatically and the images were classified by wide training. Experimental results showed that the PolSAR image classification algorithm based on the CNN is higher than that of the traditional algorithms using the same dataset. [PERSON] et al. converted the complex matrix of the PolSAR image into a real matrix of six channels to suit the input of the neural network, and designed two, cascaded, fully connected networks to map the features to a certain classifier [PERSON] et al. (2013). This algorithm further improves the accuracy of PolSAR image classification. Although the deep learning framework provides an idea to solve the problem of low utilization of rich features in PolSAR image classification, it still faces the following problems.