  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.
