Graph-based active learning for semi-supervised classification of SAR data

Kevin Miller; Jack Mauro; Jason Setiadi; Xoaquin Baca; Zhan Shi; Jeff Calder; Andrea L. Bertozzi

doi:10.1117/12.2618847

31 May 2022 Graph-based active learning for semi-supervised classification of SAR data

Kevin Miller, Jack Mauro, Jason Setiadi, Xoaquin Baca, Zhan Shi, Jeff Calder, Andrea L. Bertozzi

Proceedings Volume 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX; 120950C (2022) https://doi.org/10.1117/12.2618847
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Kevin Miller, Jack Mauro, Jason Setiadi, Xoaquin Baca, Zhan Shi, Jeff Calder, and Andrea L. Bertozzi "Graph-based active learning for semi-supervised classification of SAR data", Proc. SPIE 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX, 120950C (31 May 2022); https://doi.org/10.1117/12.2618847

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available