Additional information provided by three-dimensional (3-D) scattering centers (SCs) is useful in automatic target recognition (ATR). An approach is proposed for 3-D SC extraction from multiple-resolution synthetic aperture radar (SAR) measurements at arbitrary azimuths and elevations. This approach consists of a feature-level extraction and a signal-level optimization. In the feature-level extraction, two-dimensional (2-D) SCs are first extracted at each aspect, then 3-D SCs are coarsely generated from these 2-D SCs by a clustering method. This clustering method contains a particular distance equation and an ingenious clustering strategy which is developed based on some basic properties of scattering physics and the geometric transformation of 3-D SCs and 2-D SCs. Exploiting the sparsity of SCs in the feature domain, such a method efficiently extracts 3-D SCs. In the signal-level optimization, 3-D SC parameters are directly re-estimated using the measurement data. This improves the precision of 3-D SC parameters and provides reliable reconstructions. Finally, the experimental results of data generated by the GTD model and the high-frequency electromagnetic magnetism code exhibit the effectiveness of the proposed approach. In addition, we apply our approach to multiple-pass circle SAR. The reconstructed 3-D SCs exactly depict the shape of the target.