# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 1 Introduction  
Synthetic aperture radar (SAR) imagery has the capabilities of providing high resolution images for a large area under all-weather and all-day conditions. Therefore, it has broad applications in areas such as geological exploration, environmental monitoring, and natural disaster assessment [1]. Moreover, compared with traditional single polarized SAR images, full polarized SAR images (Pol-SAR), which contain four different Pol-SAR channels (e.g., hh, hv, vh, and vv), can provide more abundant information on land cover characteristics. It is able to provide not only the radar scattering cross section (RCS) but also the phase information of the four channels, which can reveal the scattering mechanism of targets from various aspects, such as structure and surface undulations. Hence, the classification of different lithologies based on Pol-SAR images is becoming an important tool. In the last few decades, a variety of land cover classification algorithms have been proposed [2]. They can be broadly categorized into supervised approaches, semi-supervised approaches, and unsupervised approaches according to whether manual annotations are utilized. [PERSON] proposed a maximum-likelihood based classification method for Pol-SAR images based on a complex Gaussian distribution [3]. This classifier was applied to single look Pol-SAR images and was modified to multi-look situations by [PERSON] [4]. In this work, many features are extracted from Pol-SAR images, which can describe the targets from various aspects. The application of features makes it an important field in which to conduct land cover classification based on polarimetric features. For instance, [PERSON] defined three features including entropy (H), anisotropy (A), and alpha (\(\alpha\)) by eigen decomposition. The H and \(\alpha\) are widely used for classification [5]. Freeman-Durden [6] determined three-component scattering mechanisms based on physical models. In this method, double bounce, volume, and surface scattering correspond with the double-bounce scattering from a dihedral corner reflector, randomly oriented thin cylindrical dipoles, and first-order Bragg scattering, respectively. The Freeman-Durden decomposition is useful for decomposing the scattering mechanism from naturally incoherent scatterers. In Reference [7], [PERSON] synthesized polarization decomposition, and proposed an object-based Pol-SAR image classification based on a decision tree. Through mathematical and physical analysis of Pol-SAR data, [PERSON] presented a modified coherency matrix. The classification accuracy of wetlands was improved to 92.17% by combining the modified coherency matrix and a random forest algorithm [8]. Recently, deep learning have been proven to be very effective in classification and recognition as it can extract features automatically [9; 10]. Therefore, a Pol-SAR image classification based on deep learning has attracted the attention of experts [11]. [PERSON] proposed the usage of complex valued neural networks for classification of different land in Pol-SAR images, which confirmed the possibility of deep learning applied to Pol-SAR image classification [12]. [PERSON] et al. used a deep belief network as one of the deep learning models to classify different urban land covers in a Pol-SAR image [13]. In Reference [14], [PERSON] introduced a convolutional neural network into Pol-SAR image classification. Experimental results with Pol-SAR data indicated that the deep learning methods could provide competitive results. [PERSON] proved the suitability of a stacked sparse autoencoder (SSAE) in the classification of Pol-SAR images [15]. However, this method is pixel-based, without considering the local spatial information. To solve this problem, [PERSON] et al. took all the neighborhood pixels around the cell under test as the input data, and then the Pol-SAR images were classified by SSAE [16]. This method integrated the contextual information of the neighborhood, but it has defects in practical applications especially when the target area is complex.