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Graph Neural Network for Accurate and Low-complexity SAR ATR

Authors:
Bingyi Zhang
Sasindu Wijeratne
Rajgopal Kannan
Viktor Prasanna
Carl Busart

Keywords: Synthetic aperture radar, automatic target recognition, graph neural network, low computation complexity, model pruning

Abstract:
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.

Pages: 25 to 30

Copyright: Copyright (c) IARIA, 2023

Publication date: April 24, 2023

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-68558-079-7

Location: Venice, Italy

Dates: from April 24, 2023 to April 28, 2023