## 1 Introduction  
Buildings are the place where human beings depend for survival. They are the most essential part of urban construction. Building identification and extraction from remote sensing are of great value in urban planning and land use planning. Synthetic-aperture radar (SAR) has the capability of all-time/all-weather imaging capabilities. Compared with the SAR data of a single polarization channel, full polarimetric SAR (PolSAR) can obtain more abundant backscattering information and provide more valuable feature information for the recognition and extraction of features [1]. With the acceleration of global urbanization, it is significant to accurately extract buildings from PolSAR images for urbanization development monitoring [2, 3]. In PolSAR images, buildings usually have specific characteristics in polarization features and texture features, which are vital for extraction buildings. In polarization features, Pauli decomposition parameters are commonly used and have been experimentally proven to have good accuracy in PolSAR classification [4]. The building's backscatter energy and double scattering tend to be higher than other surrounding features, showing highlighting in PualiRGB. However, polarimetric scattering information in PolSAR images is affected by speckle noise, which could deteriorate the results. Due to that, some buildings are easily confused with other objects that have the same scattering mechanism as buildings [5]. Considering the coherent speckle, statistical information for building extraction is particularly important. There are many SAR statistical distribution models, including Wishart distribution [6; 7], K distribution [8], G\({}^{0}\) distribution [9], etc. Among these SAR statistical distribution models, the G\({}^{0}\) distribution can simulate extremely heterogeneous areas, such as urban areas. Further, texture parameters based on G\({}^{0}\) distribution have been applied in PolSAR image classification and segmentation. The comprehensive utilization of two or more polarization and texture features plays an important role in improving the extraction accuracy of urban data [10; 11], and it is also one of the development trends. Various classification algorithms have been introduced into PolSAR image building extraction or urban classification to improve accuracy. Traditional machine learning methods have been applied successively to PolSAR image building extraction, including the eigenvalue-based method [12], the K-means clustering algorithm, the maximum likelihood classification method [13], support vector machines [14], the decision tree algorithm [15], the random forest algorithm [16], etc. With the development of machine learning technology, deep learning methods have been increasingly used for SAR image target extraction and classification [17; 18] in recent years. Especially, the deep learning method using CNN is almost the most widely used PolSAR deep learning classification method [19]. PolSAR data are made into a six-channel as the CNN input (RVCNN), including SPAN, the normalized polarimetric power ratio, and three relative correlation coefficients [20], which proves the suitability and potential of CNN in the classification of PolSAR. The Polarimetric-Feature-Driven Deep CNN uses the SPAN, entropy, mean alpha angle, anisotropy, and two null angle parameters of 6 null_Re [T12] and 6 null_Im [T12] as the input of the CNN [21]. The Polarimetric-Feature-Driven Deep CNN (PFDCNN) proved that the addition of polarization features is beneficial to the application of CNN in the classification of PolSAR. However, speckle noise has an effect on building extraction in PolSAR images by the CNN method. Superpixels are meaningful atomic regions that contain spatial context information, which could be generated by SLIC or other segmentation methods [22]. A superpixel is a spatially continuous collection of pixels with similar features that adhere well to surface feature boundaries.