# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 1 Introduction  
Polarimetric synthetic aperture radar (PolSAR) has become an important remote sensing tool. Besides the advantage of operating in all times and under all weather conditions, it also provides richer ground information than single-polarization SAR [1]. Along with the development of imaging techniques and the enhancing availability of PolSAR data, effective PolSAR image interpretation techniques are an urgent requirement. Land-cover classification is one of the most important tasks of PolSAR image interpretation [2]. Up to now, different PolSAR image classification approaches have been proposed, either based on statistical properties of PolSAR data [3, 4] or based on scattering mechanism identification [5, 6].  
PolSAR images not only include polarimetric information but also include spatial information. Polarimetric or spatial information cannot describe PolSAR image comprehensively, causing information (polarimetric or spatial) loss. Luckily, inspired by the complementarity between spatial and spectral features producing significant improvements in optical image classification [7], in this paper, the main characteristic of the proposed approach is that it takes advantage of both polarimetric and spatial information for classification.  
In order to utilize comprehensive polarimetric information, various polarimetric signatures obtained by target decomposition (TD) methods and algebra operations (AO) can be combinedtogether to form a high-dimensional feature space [8; 9; 10]. Moreover, so as to take advantage of spatial information, different strategies can be considered, such as using the Markov random field (MRF) model [11; 12], classifying with over-segmentation patches [13; 14], and exploiting features that contain spatial information [15; 16; 17]. Concerning the last strategy, which will be adopted in this paper, one recently emerged method is based on morphological filters [18].  
The main problem here is how to make comprehensive use of the two types of features. In this paper, this problem is solved based on the theory of feature fusion via composite kernels [19]. Compared with the traditional feature fusion method using vector stacking [20], composite kernels can tune the contribution of two types of features, exploiting inner properties more sufficiently, regardless of the weight between different features, leading the fusion more naturally.  
Our experiments are conducted with real PolSAR data sets: a Flevoland data set and a San Francisco Bay data set. In order to assess the classification performance of the proposed method, user accuracy, overall accuracy, and kappa coefficient are determined. Experimental results demonstrate that the proposed approach can more efficiently exploit both the polarimetric and the spatial information contained in PolSAR images compared with the traditional method of feature fusion. Classification performance is thus improved.  
The reminder of the paper is structured as follows. Section 2 introduces the related work of the entire classification, which is made up of three main components: polarimetric features, spatial features, and stacked feature fusion. Section 3 presents a novel feature fusion for the classification of PolSAR images based on composite kernels. In Section 4, the two experiments and data sets are described, and the results and discussion are presented. Finally, Section 5 concludes and discusses potential future studies.