The identification and extraction of building is a vital task in urban environmental planning and research. High-resolution SAR technology has the advantages of all-day, all-weather and strong penetration, and has become one of the important technical means to study urban areas. A novel method for building extraction using both associated features and SVM that based on the full polarization SAR image of GF-3 is proposed in this paper. Firstly, filtering the GF-3 image to reduce the speckles of the image, then texture features based on Span map are extracted by using GLCM. Principal component analysis is used to remove the correlation between them and select the best texture features. The normalized circular-pol correlation coefficient is introduced as the polarization feature and combines with the best texture features. Finally, the image is classified and extracted by SVM. In this paper, the extraction results are compared with the results of the texture feature building extraction method. The experimental results show that the proposed method can obtain higher extraction precision, and the extraction effect is obviously optimized.