Graph-based active learning for semi-supervised classification of SAR data
Graph-based active learning for semi-supervised classification of SAR data
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DOI：
10.1117/12.2618847
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发表时间：
2022-03
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通讯作者：
Kevin Miller;John Mauro;Jason Setiadi;Xoaquin Baca;Zhan Shi;J. Calder;A. Bertozzi
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作者：
Kevin Miller;John Mauro;Jason Setiadi;Xoaquin Baca;Zhan Shi;J. Calder;A. Bertozzi
将基于图的学习思想与神经网络方法相结合，在主动学习框架内，提出了一种新的合成孔径雷达(SAR)数据分类方法。机器学习中的基于图的方法是基于从数据构建的相似性图。当数据由场景组成的原始图像组成时，外部信息可能会使分类任务变得更加困难。近年来，神经网络方法被证明为从SAR图像中提取模式提供了一个很有前途的框架。然而，这些方法需要充足的训练数据以避免过度拟合。同时，这样的训练数据通常不适用于感兴趣的应用，例如自动目标识别(ATR)和合成孔径雷达数据。我们使用卷积神经网络变分自动编码器(CNNVAE)将SAR数据嵌入到特征空间中，然后从嵌入的数据构造相似度图，并应用基于图的半监督学习技术。CNNVAE特征嵌入和图构造不需要标注数据，减少了过度拟合，提高了图学习在低标签率下的泛化性能。此外，该方法很容易在数据标记过程中融入主动学习的人在回路中。我们给出了有希望的结果，并将它们与其他标准的机器学习方法在具有少量标记数据的ATR的运动和静止目标获取和识别(MStar)数据集上进行了比较。
We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine learning are based on a similarity graph constructed from the data. When the data consists of raw images composed of scenes, extraneous information can make the classification task more difficult. In recent years, neural network methods have been shown to provide a promising framework for extracting patterns from SAR images. These methods, however, require ample training data to avoid overfitting. At the same time, such training data are often unavailable for applications of interest, such as automatic target recognition (ATR) and SAR data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques. The CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting and improves the generalization performance of graph learning at low label rates. Furthermore, the method easily incorporates a human-in-the-loop for active learning in the data-labeling process. We present promising results and compare them to other standard machine learning methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset for ATR with small amounts of labeled data.