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Attack from the unmanned aerial vehicles (UAVs) has been the main means of high-precision strike. Therefore, classifying ground vehicles from the UAV with high accuracy is of great significance. In order to avoid the complex feature extracting process and realize the classification of UAV-to-ground vehicles in different situations, this paper proposed a method based on micro-Doppler signatures using singular value decomposition (SVD) and deep convolutional neural networks (DCNNs). First, models of UAV-to-ground vehicles are built to analyze the micro-Doppler components and Doppler signals in five different cases are given. Second, time-frequency spectrums of Doppler signals with low signal-to-noise ratios are improved after removing noise using SVD. Third, transfer-learning of pre-trained DCNNs is achieved using measured data and classification under various conditions is realized using the new-trained …