Next Article in Journal
An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping
Previous Article in Journal
UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning
Previous Article in Special Issue
Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks
Open AccessArticle

Deep Transfer Learning for Few-Shot SAR Image Classification

HRL Laboratories, Malibu, CA 90265-4797, USA
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1374;
Received: 30 April 2019 / Revised: 30 May 2019 / Accepted: 5 June 2019 / Published: 8 June 2019
(This article belongs to the Special Issue Deep Transfer Learning for Remote Sensing)
PDF [522 KB, uploaded 8 June 2019]


The reemergence of Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper, we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for a huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data are easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide a theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other competing learning approaches. View Full-Text
Keywords: transfer learning; convolutional neural network; electro-optical imaging; Synthetic Aperture Radar (SAR) imaging; optimal transport metric transfer learning; convolutional neural network; electro-optical imaging; Synthetic Aperture Radar (SAR) imaging; optimal transport metric

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Rostami, M.; Kolouri, S.; Eaton, E.; Kim, K. Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens. 2019, 11, 1374.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top