Multi-aspect synthetic aperture radar (SAR) can obtain more complete scattering characteristics of the target by observing in different aspects, so it is useful for accurate interpretation. At present, man-made target extraction based on multi-aspect SAR images mostly uses anisotropic scattering features. For common man-made targets, there are some special structures such as dihedral corners, so strong scattering features also exist in SAR images. Based on this, a new method for man-made target extraction based on multi-aspect SAR images is proposed, the method combines the strong scattering features at specific aspects and anisotropic scattering features at different aspects. For strong scattering feature, a fast fuzzy C-means clustering (FCM) is used to extract in every sub-aperture image. For anisotropic scattering feature, the aspect entropy is used to represent the anisotropy of pixels in different sub-apertures. Finally, by merging the extraction results of the two features, more accurate extraction can be obtained. In order to verify the effectiveness and practicability of the proposed method, a large number of airborne L-band and Ku-band circular SAR (CSAR) measured data are processed, and the experiments show that the results of the proposed method are more accurate than that of single feature.