Deep semi-supervised label propagation for SAR image classification
Deep semi-supervised label propagation for SAR image classification
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DOI：
10.1117/12.2663665
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发表时间：
2023-06
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影响因子：
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通讯作者：
Joshua Enwright;Harris Hardiman-Mostow;J. Calder;A. Bertozzi
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文献类型：
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作者：
Joshua Enwright;Harris Hardiman-Mostow;J. Calder;A. Bertozzi
合成孔径雷达（SAR）数据的自动目标识别是一个具有挑战性的问题，由于图像的复杂性和获取标签的困难。最近的工作1使用卷积变分自动编码器来提取相关特征，然后在SAR数据的基于图的主动学习框架中构建相似图。在这项工作中，我们提出了两种新的SAR数据分类方法，使用卷积神经网络（CNN）特征提取以及基于图的半监督学习技术，以端到端的方式，可以在SAR ATR中常见的小标记数据集制度中提供更好的分类性能。首先，我们介绍了拉普拉斯输出激活神经网络（LOAN网络）作为一种直接优化特征嵌入的方法，用于基于图的半监督学习技术。接下来，我们将介绍伪标签传播神经网络（PsLaPN网络）作为一种廉价的方法来提高训练信号，并对抗神经网络中的过度自信和模型校准不良。我们提出了一种新的简单公式的推导，用于直接和有效地计算基于图的算法（如标签传播2）的输出导数，用于我们的网络的训练。我们在SAR数据集OpenSARShip上测试了所提出的端到端网络的主动学习，其中两种方法都超过了以前的最先进水平。
Automatic target recognition with synthetic aperture radar (SAR) data is a challenging problem due to the complexity of the images and the difficulty in acquiring labels. Recent work1 used a convolutional variational autoencoder to extract relevant features prior to constructing a similarity graph in a graph-based active learning framework for SAR data. In this work we present two novel methods for classifying SAR data that use convolutional neural network (CNN) feature extraction together with techniques from graph-based semi-supervised learning in an end-to-end manner that can provide improved classification performance in the small labeled dataset regimes that are common in SAR ATR. First, we introduce Laplace Output Activation Neural Networks (LOAN Networks) as a way of directly optimizing feature embeddings for use with graph-based semi-supervised learning techniques. Next, we introduce Pseudo Label Propagation Neural Networks (PsLaPN Networks) as a inexpensive way to both boost the training signal as well as combat overconfidence and poor model calibration in neural networks. We present a novel derivation of simple formulas for the direct and efficient computation of derivatives of the outputs of graph-based algorithms like label propagation2 for use in the training of our networks. We test the proposed end-to-end networks for active learning on OpenSARShip, a SAR dataset, where both methods surpass the previous state-of-the-art.