Graph-based Active Learning for Semi-supervised Classification of SAR Data∗

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAlgorithms for Synthetic Aperture Radar Imagery XXIX
EditorsEdmund Zelnio, Frederick D. Garber
PublisherSPIE
ISBN (Electronic)9781510650664
DOIs
StatePublished - 2022
EventAlgorithms for Synthetic Aperture Radar Imagery XXIX 2022 - Virtual, Online
Duration: Jun 6 2022Jun 12 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12095
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAlgorithms for Synthetic Aperture Radar Imagery XXIX 2022
CityVirtual, Online
Period6/6/226/12/22

Bibliographical note

Funding Information:
The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset11 was collected by Sandia National Laboratory in a project that was jointly sponsored by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) in 1998. For an overview tutorial of MSTAR, we refer to.42 The dataset contains 6,874 images of 10 types of military vehicles (Armored Personnel Carrier: BMP-2, BRDM-2, BTR-60, and BTR-70; Tank: T-62, T-72; Weapon System: 2S1; Air Defense Unit: ZSU-234; Truck: ZIL-131; Bulldozer: D7). A Sandia X-band radar operating at 9.60GHz with a bandwidth of 0.591GHz was used to collect the data; the range and cross range resolution were both 0.3047m. We follow a standard training and testing split according to the angle at which the SAR data was collected; namely, the training data was obtained

Funding Information:
This research is funded by an academic grant from the National Geospatial-Intelligence Agency (Award No. #HM0476-21-1-0003 , Project Title: Graph-based Active Learning for Semi-supervised Classification of SAR Data). Approved for public release, 22-346. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NGA. KM was supported by the DOD’s NDSEG Fellowship. JC was supported by NSF DMS-1944925, the Alfred P. Sloan Foundation, and a McKnight Presidential Fellowship. ALB was supported by DARPA Award number FA8750-18-2-0066 and NSF grant NSF DMS-1952339.

Publisher Copyright:
© 2022 SPIE

Keywords

  • Active Learning
  • Graph-Based Learning
  • Synthetic Aperture Radar

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