The past years have witnessed a revival of neural network and learning strategies. These models configure multiple hidden layers hierarchically and require large amounts of labeled samples to estimate the model parameters. It is yet difficult to be met for target recognition under the realistic environments. For either space borne or airborne radars, collecting multiple samples with label information is very expensive and difficult. In addition, the huge computational cost and poor speed of convergence limit the practical applications. To address the problems, this article presents a new thought of receptive, under which a special hierarchy of feedforward neural network has been built. The proposed strategy consists of two sequential modules: 1) feature generation and 2) feature refinement. We first build pairwise baseline signals by means of the Riesz transform along the range and the azimuth, and extend them to a family of receptive signals using the bandpass filter bank. The input SAR image is then generally convoluted with the set of receptive signals to extract the global features. Certain kinds of information can be then exploited. We make the receptive signals predefined, rather than learned automatically, to handle the environment of a small sample size. In addition, the expert knowledge can be transmitted into the neural network. The resulting features are further refined by a special unit, wherein the input neurons and the latent states are bridged by the weights and the bias randomly generated. They are fixed during the training process. On the other hand, we cast the latent state into the Hilbert space, forming the kernel version of refinement. We aim to achieve the comparable or even better performance yet with limited training resources.