  Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the key
technique for remote sensing image recognition. The state-of-the-art works
exploit the deep convolutional neural networks (CNNs) for SAR ATR, leading to
high computation costs. These deep CNN models are unsuitable to be deployed on
resource-limited platforms. In this work, we propose a graph neural network
(GNN) model to achieve accurate and low-latency SAR ATR. We transform the input
SAR image into the graph representation. The proposed GNN model consists of a
stack of GNN layers that operates on the input graph to perform target
classification. Unlike the state-of-the-art CNNs, which need heavy convolution
operations, the proposed GNN model has low computation complexity and achieves
comparable high accuracy. The GNN-based approach enables our proposed
\emph{input pruning} strategy. By filtering out the irrelevant vertices in the
input graph, we can reduce the computation complexity. Moreover, we propose the
\emph{model pruning} strategy to sparsify the model weight matrices which
further reduces the computation complexity. We evaluate the proposed GNN model
on the MSTAR dataset and ship discrimination dataset. The evaluation results
show that the proposed GNN model achieves 99.38\% and 99.7\% classification
accuracy on the above two datasets, respectively. The proposed pruning
strategies can prune 98.6\% input vertices and 97\% weight entries with
negligible accuracy loss. Compared with the state-of-the-art CNNs, the proposed
GNN model has only 1/3000 computation cost and 1/80 model size.
