Graph Neural Network for Accurate and Low-complexity SAR ATR

• URL: http://arxiv.org/abs/2305.07119v1
• Date: Thu, 11 May 2023 20:17:41 GMT
• Title: Graph Neural Network for Accurate and Low-complexity SAR ATR
• Authors: Bingyi Zhang, Sasindu Wijeratne, Rajgopal Kannan, Viktor Prasanna, Carl Busart
• Abstract summary: We propose a graph neural network (GNN) model to achieve accurate and low-latency SAR ATR. The proposed GNN model has low computation complexity and achieves comparable high accuracy. Compared with the state-of-the-art CNNs, the proposed GNN model has only 1/3000 computation cost and 1/80 model size.
• Score: 2.9766397696234996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Graph Mining under Data scarcity [6.229055041065048]
  We propose an Uncertainty Estimator framework that can be applied on top of any generic Graph Neural Networks (GNNs) We train these models under the classic episodic learning paradigm in the $n$-way, $k$-shot fashion, in an end-to-end setting. Our method outperforms the baselines, which demonstrates the efficacy of the Uncertainty Estimator for Few-shot node classification on graphs with a GNN.
  arXiv  Detail & Related papers  (2024-06-07T10:50:03Z)
• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA [3.251765107970636]
  Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key technique for remote-sensing image recognition. The state-of-the-art convolutional neural networks (CNNs) for SAR ATR suffer from emphhigh computation cost and emphlarge memory footprint. We propose a comprehensive GNN-based model-architecture co-design on FPGA to address the above issues.
  arXiv  Detail & Related papers  (2023-01-04T05:35:30Z)
• Robust Graph Neural Networks using Weighted Graph Laplacian [1.8292714902548342]
  Graph neural network (GNN) is vulnerable to noise and adversarial attacks in input data. We propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWL-GNN)
  arXiv  Detail & Related papers  (2022-08-03T05:36:35Z)
• Network In Graph Neural Network [9.951298152023691]
  We present a model-agnostic methodology that allows arbitrary GNN models to increase their model capacity by making the model deeper. Instead of adding or widening GNN layers, NGNN deepens a GNN model by inserting non-linear feedforward neural network layer(s) within each GNN layer.
  arXiv  Detail & Related papers  (2021-11-23T03:58:56Z)
• Training Graph Neural Networks with 1000 Layers [133.84813995275988]
  We study reversible connections, group convolutions, weight tying, and equilibrium models to advance the memory and parameter efficiency of GNNs. To the best of our knowledge, RevGNN-Deep is the deepest GNN in the literature by one order of magnitude.
  arXiv  Detail & Related papers  (2021-06-14T15:03:00Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• A Unified Lottery Ticket Hypothesis for Graph Neural Networks [82.31087406264437]
  We present a unified GNN sparsification (UGS) framework that simultaneously prunes the graph adjacency matrix and the model weights. We further generalize the popular lottery ticket hypothesis to GNNs for the first time, by defining a graph lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network.
  arXiv  Detail & Related papers  (2021-02-12T21:52:43Z)
• Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case [93.37576644429578]
  Graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice. We provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
  arXiv  Detail & Related papers  (2020-06-25T00:45:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.