Unmanned aerial vehicle (UAV) has become an important radar target recently because of its wide applications and potential security threats. Traditionally, visual features such as spectrogram were often extracted for human operators to identify the micro-Doppler signature (mDS) of UAVs, i.e. sinusoidal modulation. In this paper, the authors aim to design a system for machine automatic classification of UAVs from other targets, particularly from birds as both UAVs and birds are small and slow-moving radar targets. Most existing mDS representations such as spectrogram, cepstrogram and cadence velocity diagram discard the phase spectrum, and only make use of the magnitude spectrum. What’s more, people often take the logarithm of the spectrum to enlarge the weak mDS but without sufficient care, as noise may be enlarged at the same time. The authors thus propose a regularized 2-D complex-log-Fourier transform to address these problems. Furthermore, the authors propose an object-oriented dimension-reduction technique: subspace reliability analysis, which directly removes the unreliable feature dimensions of two class-conditional covariance matrices in two separate subspaces. On the benchmark dataset, the proposed approach demonstrates better performance than the state-of-the-art approaches. More specifically, the proposed approach significantly reduces the equal error rate of the second best approach, cadence velocity diagram, from 6.68% to 3.27%.